This document provides an overview of predictive analytics and its growing use in the insurance industry. Predictive analytics uses statistical techniques and data mining to develop models that predict future events or behaviors. Insurers are increasingly using predictive analytics in marketing, underwriting, and claims functions. Key drivers of its adoption include technological advances enabling complex analytics, greater data availability, insurers' desire for growth in slow markets, and seeking competitive advantages. The document discusses various predictive analytic applications and their potential benefits for insurers.
This document discusses predictive analytics and provides an overview of Oracle's predictive analytics tools.
It argues that predictive analytics is commonly misunderstood as only predicting the future, but can also be used to predict the present based on existing data patterns. It proposes a new conceptual classification of predictive analytics into "predicting the present" and "shaping the future". The document then provides examples of how Oracle Data Mining can be used to predict things in the present like customer preferences, fraud detection, and credit scoring. It also discusses how Oracle Real-Time Decisions integrates predictive analytics into real-time processes.
Forrester big data_predictive_analyticsShyam Sarkar
The document provides an overview of the big data predictive analytics market and solutions. It discusses how predictive analytics can help organizations reduce risks, make better decisions, and deliver personalized customer experiences by analyzing big data. The document evaluates 10 leading vendors of big data predictive analytics solutions based on their current offerings, strategies, and market presence. It finds that the ability to handle big data, easy-to-use modeling tools, and a wide choice of algorithms differentiate the leading solutions in this growing market.
This document provides an overview of predictive analytics and its growing use in the insurance industry. It discusses key drivers for insurers' adoption of predictive analytics, including technological advances, data availability, seeking growth in slow-growth markets, and gaining competitive advantage. The document outlines how insurers use predictive analytics in marketing, underwriting, and claims to improve hit ratios, retention ratios, identify fraudulent claims, and prioritize claims processing. It provides details on the predictive analytic process of data mining, model development using regression and advanced models, and model validation. The advantages and disadvantages of predictive analytics for insurers are also discussed.
This document provides an overview of predictive analytics. It discusses what predictive analytics is and how it is used by organizations to make smarter decisions about customers. Predictive analytics uses historical data and statistical techniques to predict future outcomes and automate decisions. Examples are given of how predictive analytics has helped industries like financial services, insurance, telecommunications, retail, and healthcare improve customer decisions and outcomes.
Serene Zawaydeh - Big Data -Investment -WaveletsSerene Zawaydeh
Big data solutions are being implemented in the investment industry among other industries, allowing processing of a large volume of variables including real time changes.
In addition to highlighting current applications of big data in the investment industry, this paper identifies applications of Wavelets in finance and Big Data. Wavelets are used for the analysis of non stationary signals. Academic studies proved the benefits of using Wavelets for forecasting financial time series, data mining among other applications.
Requirements Workshop -Text Analytics System - Serene ZawaydehSerene Zawaydeh
This document provides an overview of a requirements workshop for a text analytics system. It discusses preparing for the workshop by interviewing stakeholders and understanding existing processes. The workshop would explore business requirements like delivery timeline and budget, and requirements for the text analytics system like processing unstructured data from different communication channels. Strengths of a requirements workshop include gaining agreement on priorities, but weaknesses include potential issues from stakeholders not being identified prior to the workshop.
This document discusses moving from business intelligence to predictive analytics. It introduces predictive analytics and how they can automatically discover patterns in data to predict trends or future behavior. Predictive analytics turn uncertainty about the future into usable probabilities. The document also discusses how predictive analytics can be applied in operations through decision management, which is a proven approach to deploy and apply predictive analytics at decision points.
This document discusses predictive analytics and provides an overview of Oracle's predictive analytics tools.
It argues that predictive analytics is commonly misunderstood as only predicting the future, but can also be used to predict the present based on existing data patterns. It proposes a new conceptual classification of predictive analytics into "predicting the present" and "shaping the future". The document then provides examples of how Oracle Data Mining can be used to predict things in the present like customer preferences, fraud detection, and credit scoring. It also discusses how Oracle Real-Time Decisions integrates predictive analytics into real-time processes.
Forrester big data_predictive_analyticsShyam Sarkar
The document provides an overview of the big data predictive analytics market and solutions. It discusses how predictive analytics can help organizations reduce risks, make better decisions, and deliver personalized customer experiences by analyzing big data. The document evaluates 10 leading vendors of big data predictive analytics solutions based on their current offerings, strategies, and market presence. It finds that the ability to handle big data, easy-to-use modeling tools, and a wide choice of algorithms differentiate the leading solutions in this growing market.
This document provides an overview of predictive analytics and its growing use in the insurance industry. It discusses key drivers for insurers' adoption of predictive analytics, including technological advances, data availability, seeking growth in slow-growth markets, and gaining competitive advantage. The document outlines how insurers use predictive analytics in marketing, underwriting, and claims to improve hit ratios, retention ratios, identify fraudulent claims, and prioritize claims processing. It provides details on the predictive analytic process of data mining, model development using regression and advanced models, and model validation. The advantages and disadvantages of predictive analytics for insurers are also discussed.
This document provides an overview of predictive analytics. It discusses what predictive analytics is and how it is used by organizations to make smarter decisions about customers. Predictive analytics uses historical data and statistical techniques to predict future outcomes and automate decisions. Examples are given of how predictive analytics has helped industries like financial services, insurance, telecommunications, retail, and healthcare improve customer decisions and outcomes.
Serene Zawaydeh - Big Data -Investment -WaveletsSerene Zawaydeh
Big data solutions are being implemented in the investment industry among other industries, allowing processing of a large volume of variables including real time changes.
In addition to highlighting current applications of big data in the investment industry, this paper identifies applications of Wavelets in finance and Big Data. Wavelets are used for the analysis of non stationary signals. Academic studies proved the benefits of using Wavelets for forecasting financial time series, data mining among other applications.
Requirements Workshop -Text Analytics System - Serene ZawaydehSerene Zawaydeh
This document provides an overview of a requirements workshop for a text analytics system. It discusses preparing for the workshop by interviewing stakeholders and understanding existing processes. The workshop would explore business requirements like delivery timeline and budget, and requirements for the text analytics system like processing unstructured data from different communication channels. Strengths of a requirements workshop include gaining agreement on priorities, but weaknesses include potential issues from stakeholders not being identified prior to the workshop.
This document discusses moving from business intelligence to predictive analytics. It introduces predictive analytics and how they can automatically discover patterns in data to predict trends or future behavior. Predictive analytics turn uncertainty about the future into usable probabilities. The document also discusses how predictive analytics can be applied in operations through decision management, which is a proven approach to deploy and apply predictive analytics at decision points.
Gain insights from data analytics and take action! Learn why everyone is making a big deal about big data in healthcare and how data analytics creates action.
This document provides an overview of data analytics including:
- The basics of data analytics including analytics definitions and the need for data analytics due to increasing data volumes.
- Descriptions of different types of analytics including descriptive, diagnostic, predictive, and prescriptive analytics and their purposes.
- An overview of the data analytics lifecycle including phases such as data preparation, model planning, model building, and communication of results.
Machine Learning for Business - Eight Best Practices for Getting StartedBhupesh Chaurasia
This document provides an overview of best practices for organizations getting started with machine learning. It discusses 8 best practices: 1) Learn the predictive thought process, 2) Focus on specific use cases, 3) Look for the right predictive tooling, 4) Get training on machine learning techniques, 5) Remember that good quality data is important, 6) Establish model governance processes, 7) Put machine learning models into action, and 8) Manage, monitor and optimize models continuously. The document provides details and examples for each best practice to help organizations successfully implement machine learning.
Uses of analytics in the field of BankingNiveditasri N
Analytics refers to the systematic analysis of data to derive meaningful conclusions and insights. In banking, analytics is used for applications like customer segmentation, risk modeling, fraud prevention, identifying transaction channels, and predicting customer lifetime value. It allows banks to better understand customers, assess risks, prevent fraud, optimize operations, and increase customer loyalty and profits.
Data Mining - Health Insurance - Jabran NoorJabran Noor
Data mining techniques can help healthcare insurers improve profits, predict trends, and gain competitive advantages by analyzing large volumes of customer data. Traditional actuarial analyses like burning cost analysis and regression models provide useful but limited insights, while data mining techniques like dimensionality reduction, visualization, clustering, classification, and association rule mining can provide deeper insights into relationships within the data. Applying multiple data mining techniques and validating findings with experts can help supplement traditional actuarial methods for a more comprehensive understanding of business dynamics.
This document discusses concerns about the use of big data in consumer credit scoring. It summarizes a study conducted by the National Consumer Law Center that found significant inaccuracies in consumer data reports from several big data brokers. The document also analyzes whether big data scoring complies with laws like the Fair Credit Reporting Act and could result in discriminatory impacts. Finally, it reviews loan products using big data underwriting and finds that they fail to provide genuinely affordable alternatives to payday loans. Overall, the document concludes that big data has not lived up to its promise to expand access to credit for underserved consumers in a fair, accurate and beneficial way.
Banks Betting on Big Data Analytics and Real-Time Execution to Better Engage ...SAP Analytics
Banks are making customer centricity a top priority over the next two years and see data analytics technologies like predictive analytics, data visualization, and real-time data processing as key to achieving this, with over half of large bank executives surveyed seeing each of these as important; however, most executives also admit their current capabilities are not mature enough to fully support their customer-focused strategies.
Fraud detection is a popular application of Machine Learning. But is not that obvious and not that common as it seems. I'll tell how QuantUp implemented it for WARTA insurance company (a subsidiary of Talanx International AG).
The models developed gave between 10% and 30% of reduction of losses. The project was not a simple one because of the complex process of handling claims and using really rich dataset. The tools applied were R (modeling) and DataWalk (data peparation). You will learn what is important in development of such solutions in general, what was difficult in this particular project, and how to overcome possible difficulties in similar projects.
Banks can leverage machine learning models to increase value through stronger customer acquisition, higher customer lifetime value, and lower operating costs. AI-powered decision making allows for personalized experiences, continuous customer engagement, automated document processing, and early risk detection. Advanced analytical models can be organized around significant elements like the customer lifecycle to benefit banks.
Leading Compliance Monitoring Activities to Assess Fraud and Corruption RisksRachel Hamilton
This document discusses leveraging forensic data analytics (FDA) to detect fraud. It notes that traditional audits only detect around 50% of fraud, demonstrating a need for improved analytics. FDA incorporates collecting both structured and unstructured data from sources like ERP systems, CRM, and documents to identify improper payments and behavior patterns. Effective FDA programs incorporate rule-based queries, statistical analysis, text searching, and data visualization. The document provides examples of how companies can design FDA programs, including gathering diverse data sources, processing the data, analyzing it for risk, and delivering results. It emphasizes that continuous monitoring is important for executive visibility, process improvements, and advanced fraud control.
predictive-analytics-the-silver-bullet-in-efficient-risk-management-for-banksArup Das
This document discusses how predictive analytics can help banks improve risk management. It begins by outlining the major risks banks face and the regulatory requirements around risk management. It then discusses how predictive analytics can enhance various aspects of enterprise risk management, including improving credit decisioning, enhancing credit quality, and enabling a 360-degree view of customers. The document provides examples of how social network analysis and big data can generate insights to better identify fraud and risk. Overall, the document argues that predictive analytics, when embedded into risk management frameworks, can help banks more efficiently identify and respond to risks.
Predictive Analytics: The Next Wave in Business IntelligencePerficient, Inc.
We discuss how Predictive Analytics enables decision makers to predict future events and proactively act on that insight to drive better business outcomes and deliver the insight needed to answer key business questions:
- How to reduce churn and retain the most loyal customers to maximize profitability (predict which customers are most likely to leave and which are most loyal)
- How to detect and ultimately prevent fraudulent activity
- Which factors are most likely to drive customers to choose my product over the competitor’s?
- How to integrate Predictive Analytics with an existing Business Intelligence platform
Presenter Tom Lennon is Director of Perficient's National Business Intelligence Competency Center.
Predictive analytics uses statistical techniques and business intelligence technologies to uncover relationships within large datasets to predict future behaviors or outcomes. While predictive analytics can provide benefits like reducing customer churn or improving marketing campaign response rates, it is not widely used due to complexity, underestimating value, high software costs, and reliance on good quality data. The document outlines best practices for predictive analytics including focusing on data management, expecting incremental improvements over time, measuring impact using business metrics, and gaining executive sponsorship for projects.
1) Analytics use is on the rise in businesses, with more companies using it across their entire enterprise. Two-thirds have appointed analytics leaders and over half see senior leadership as committed to analytics.
2) Predictive analytics use has nearly tripled since 2009 as companies seek to anticipate the future. However, the demand for predictive capabilities exceeds current supply.
3) Analytics is primarily being used in customer-focused areas like marketing, sales, and customer retention to improve experiences and decision-making. However, companies still need to better link analytics insights to key business outcomes.
Ten Commandments for Tackling Fraud: The Role of Big Data and Predictive Anal...CA Technologies
Accurate enterprise-wide data combined with data-driven fraud analytics can have a transformational effect on banking and related industries. This presentation provides tips and insights on using technologies like neural network predictive modeling, user behavior-based pattern recognition and statistical big data analytics to reduce the risk of fraudulent activities in the enterprise.
For more information on CA Security solutions, please visit: http://bit.ly/10WHYDm
This document contains confidential information belonging to AAUM. Any disclosure of this confidential information would damage AAUM. AAUM retains ownership of all confidential information contained in this document, regardless of the media. This document contains claim analytics data that AAUM considers confidential.
driving_business_value_from_real_time_streaming_analyticsJane Roberts
Real-time streaming analytics processes data as it is generated to identify patterns and insights without disrupting existing systems. This allows businesses to act with certainty on the latest data and make complex decisions more easily. The document discusses use cases like predictive maintenance, customer behavior analytics, and internet of things analytics. It also introduces StreamAnalytix, a streaming analytics platform that can build applications across industries using a visual interface and integration with Hadoop.
The document discusses a proposed middleware platform to enable straight-through-processing in the P&C insurance industry by connecting disparate systems. It outlines the current lack of connectivity between insurers, brokers, and other participants. The objective is to utilize smart form technology on a middleware platform to provide connectivity. The presentation provides an overview of the system foundation, technology, structure in SQL Server, and Sagent integration. It discusses areas of focus, development strategy, target market, and competitive positioning.
The document discusses key trends in digital marketing and lessons learned from 2012. It analyzes data from marketing surveys to compare the practices of "Top Performers" to "Everyone Else". Top Performers are more likely to invest in marketing automation and focus on personalization. While adoption of automation grew in 2012, many organizations still rely on outdated batch email campaigns. The document recommends simplifying automation implementation and focusing on people, process and strategy in addition to technology.
This document discusses the rise of predictive analytics and its value in enterprise decision making. It begins by explaining how predictive analytics has expanded from niche uses to a widely adopted competitive technique, fueled by big data, improved analytics tools, and demonstrated successes. A classic example given is credit scoring, which uses predictive models to assess credit risk. The document then provides examples of other areas where predictive models generate value, such as marketing, customer retention, pricing, and fraud prevention. It discusses how effective predictive models are built by using statistical techniques on data that describes predictive factors and outcomes. The document argues that predictive models provide the most value when applied to processes involving large volumes of similar decisions that have significant financial or other impacts, and where relevant electronic
Gain insights from data analytics and take action! Learn why everyone is making a big deal about big data in healthcare and how data analytics creates action.
This document provides an overview of data analytics including:
- The basics of data analytics including analytics definitions and the need for data analytics due to increasing data volumes.
- Descriptions of different types of analytics including descriptive, diagnostic, predictive, and prescriptive analytics and their purposes.
- An overview of the data analytics lifecycle including phases such as data preparation, model planning, model building, and communication of results.
Machine Learning for Business - Eight Best Practices for Getting StartedBhupesh Chaurasia
This document provides an overview of best practices for organizations getting started with machine learning. It discusses 8 best practices: 1) Learn the predictive thought process, 2) Focus on specific use cases, 3) Look for the right predictive tooling, 4) Get training on machine learning techniques, 5) Remember that good quality data is important, 6) Establish model governance processes, 7) Put machine learning models into action, and 8) Manage, monitor and optimize models continuously. The document provides details and examples for each best practice to help organizations successfully implement machine learning.
Uses of analytics in the field of BankingNiveditasri N
Analytics refers to the systematic analysis of data to derive meaningful conclusions and insights. In banking, analytics is used for applications like customer segmentation, risk modeling, fraud prevention, identifying transaction channels, and predicting customer lifetime value. It allows banks to better understand customers, assess risks, prevent fraud, optimize operations, and increase customer loyalty and profits.
Data Mining - Health Insurance - Jabran NoorJabran Noor
Data mining techniques can help healthcare insurers improve profits, predict trends, and gain competitive advantages by analyzing large volumes of customer data. Traditional actuarial analyses like burning cost analysis and regression models provide useful but limited insights, while data mining techniques like dimensionality reduction, visualization, clustering, classification, and association rule mining can provide deeper insights into relationships within the data. Applying multiple data mining techniques and validating findings with experts can help supplement traditional actuarial methods for a more comprehensive understanding of business dynamics.
This document discusses concerns about the use of big data in consumer credit scoring. It summarizes a study conducted by the National Consumer Law Center that found significant inaccuracies in consumer data reports from several big data brokers. The document also analyzes whether big data scoring complies with laws like the Fair Credit Reporting Act and could result in discriminatory impacts. Finally, it reviews loan products using big data underwriting and finds that they fail to provide genuinely affordable alternatives to payday loans. Overall, the document concludes that big data has not lived up to its promise to expand access to credit for underserved consumers in a fair, accurate and beneficial way.
Banks Betting on Big Data Analytics and Real-Time Execution to Better Engage ...SAP Analytics
Banks are making customer centricity a top priority over the next two years and see data analytics technologies like predictive analytics, data visualization, and real-time data processing as key to achieving this, with over half of large bank executives surveyed seeing each of these as important; however, most executives also admit their current capabilities are not mature enough to fully support their customer-focused strategies.
Fraud detection is a popular application of Machine Learning. But is not that obvious and not that common as it seems. I'll tell how QuantUp implemented it for WARTA insurance company (a subsidiary of Talanx International AG).
The models developed gave between 10% and 30% of reduction of losses. The project was not a simple one because of the complex process of handling claims and using really rich dataset. The tools applied were R (modeling) and DataWalk (data peparation). You will learn what is important in development of such solutions in general, what was difficult in this particular project, and how to overcome possible difficulties in similar projects.
Banks can leverage machine learning models to increase value through stronger customer acquisition, higher customer lifetime value, and lower operating costs. AI-powered decision making allows for personalized experiences, continuous customer engagement, automated document processing, and early risk detection. Advanced analytical models can be organized around significant elements like the customer lifecycle to benefit banks.
Leading Compliance Monitoring Activities to Assess Fraud and Corruption RisksRachel Hamilton
This document discusses leveraging forensic data analytics (FDA) to detect fraud. It notes that traditional audits only detect around 50% of fraud, demonstrating a need for improved analytics. FDA incorporates collecting both structured and unstructured data from sources like ERP systems, CRM, and documents to identify improper payments and behavior patterns. Effective FDA programs incorporate rule-based queries, statistical analysis, text searching, and data visualization. The document provides examples of how companies can design FDA programs, including gathering diverse data sources, processing the data, analyzing it for risk, and delivering results. It emphasizes that continuous monitoring is important for executive visibility, process improvements, and advanced fraud control.
predictive-analytics-the-silver-bullet-in-efficient-risk-management-for-banksArup Das
This document discusses how predictive analytics can help banks improve risk management. It begins by outlining the major risks banks face and the regulatory requirements around risk management. It then discusses how predictive analytics can enhance various aspects of enterprise risk management, including improving credit decisioning, enhancing credit quality, and enabling a 360-degree view of customers. The document provides examples of how social network analysis and big data can generate insights to better identify fraud and risk. Overall, the document argues that predictive analytics, when embedded into risk management frameworks, can help banks more efficiently identify and respond to risks.
Predictive Analytics: The Next Wave in Business IntelligencePerficient, Inc.
We discuss how Predictive Analytics enables decision makers to predict future events and proactively act on that insight to drive better business outcomes and deliver the insight needed to answer key business questions:
- How to reduce churn and retain the most loyal customers to maximize profitability (predict which customers are most likely to leave and which are most loyal)
- How to detect and ultimately prevent fraudulent activity
- Which factors are most likely to drive customers to choose my product over the competitor’s?
- How to integrate Predictive Analytics with an existing Business Intelligence platform
Presenter Tom Lennon is Director of Perficient's National Business Intelligence Competency Center.
Predictive analytics uses statistical techniques and business intelligence technologies to uncover relationships within large datasets to predict future behaviors or outcomes. While predictive analytics can provide benefits like reducing customer churn or improving marketing campaign response rates, it is not widely used due to complexity, underestimating value, high software costs, and reliance on good quality data. The document outlines best practices for predictive analytics including focusing on data management, expecting incremental improvements over time, measuring impact using business metrics, and gaining executive sponsorship for projects.
1) Analytics use is on the rise in businesses, with more companies using it across their entire enterprise. Two-thirds have appointed analytics leaders and over half see senior leadership as committed to analytics.
2) Predictive analytics use has nearly tripled since 2009 as companies seek to anticipate the future. However, the demand for predictive capabilities exceeds current supply.
3) Analytics is primarily being used in customer-focused areas like marketing, sales, and customer retention to improve experiences and decision-making. However, companies still need to better link analytics insights to key business outcomes.
Ten Commandments for Tackling Fraud: The Role of Big Data and Predictive Anal...CA Technologies
Accurate enterprise-wide data combined with data-driven fraud analytics can have a transformational effect on banking and related industries. This presentation provides tips and insights on using technologies like neural network predictive modeling, user behavior-based pattern recognition and statistical big data analytics to reduce the risk of fraudulent activities in the enterprise.
For more information on CA Security solutions, please visit: http://bit.ly/10WHYDm
This document contains confidential information belonging to AAUM. Any disclosure of this confidential information would damage AAUM. AAUM retains ownership of all confidential information contained in this document, regardless of the media. This document contains claim analytics data that AAUM considers confidential.
driving_business_value_from_real_time_streaming_analyticsJane Roberts
Real-time streaming analytics processes data as it is generated to identify patterns and insights without disrupting existing systems. This allows businesses to act with certainty on the latest data and make complex decisions more easily. The document discusses use cases like predictive maintenance, customer behavior analytics, and internet of things analytics. It also introduces StreamAnalytix, a streaming analytics platform that can build applications across industries using a visual interface and integration with Hadoop.
The document discusses a proposed middleware platform to enable straight-through-processing in the P&C insurance industry by connecting disparate systems. It outlines the current lack of connectivity between insurers, brokers, and other participants. The objective is to utilize smart form technology on a middleware platform to provide connectivity. The presentation provides an overview of the system foundation, technology, structure in SQL Server, and Sagent integration. It discusses areas of focus, development strategy, target market, and competitive positioning.
The document discusses key trends in digital marketing and lessons learned from 2012. It analyzes data from marketing surveys to compare the practices of "Top Performers" to "Everyone Else". Top Performers are more likely to invest in marketing automation and focus on personalization. While adoption of automation grew in 2012, many organizations still rely on outdated batch email campaigns. The document recommends simplifying automation implementation and focusing on people, process and strategy in addition to technology.
This document discusses the rise of predictive analytics and its value in enterprise decision making. It begins by explaining how predictive analytics has expanded from niche uses to a widely adopted competitive technique, fueled by big data, improved analytics tools, and demonstrated successes. A classic example given is credit scoring, which uses predictive models to assess credit risk. The document then provides examples of other areas where predictive models generate value, such as marketing, customer retention, pricing, and fraud prevention. It discusses how effective predictive models are built by using statistical techniques on data that describes predictive factors and outcomes. The document argues that predictive models provide the most value when applied to processes involving large volumes of similar decisions that have significant financial or other impacts, and where relevant electronic
The document discusses a Forrester research report on big data predictive analytics solutions. It finds that the market is growing as organizations seek to harness big data for predictive analytics to improve business outcomes. Key differentiators among vendors include their tools for handling big data, modeling capabilities, and choice of algorithms. Forrester expects the market to remain competitive with new entrants over the next three years.
The document discusses how corporations have become more specialized and dependent on business partners through outsourcing and value chain transformation. It argues that B2B integration and communication is now critical for business operations, as internal applications rely on external data from partners. The failure of B2B platforms could significantly impact supply chains and business processes in industries like automotive and manufacturing. High availability of B2B architecture is important to avoid disruptions.
Hexaware Technologies' margins have troughed as investments made over the last three quarters eroded EBITDA margins by 713 basis points. The analyst expects margins to bottom out in Q3CY14 at 16% and then improve steadily through CY15. Revenue growth is expected to improve to high single digits in CY14 and mid-teens growth in CY15, accelerating on new deal wins. The analyst upgrades Hexaware to "Accumulate" and raises the target price to Rs160, citing bottoming margins and steady improvement, as well as revenue momentum acceleration in CY15.
The document discusses SharePoint 2013's distributed cache service. The distributed cache service provides in-memory caching for features like authentication, security trimming, page load performance, and social features. It improves performance by storing frequently accessed data like session tokens, user profiles, and viewstate across servers in the farm. The document provides guidance on configuring a distributed cache service for farms of different sizes, including the benefits of proper configuration and potential issues to address.
This document discusses several topics related to business intelligence and analytics, including:
1) Identifying "trim tabs" or small areas in an organization that can provide maximum value through analytics by understanding a company's business model.
2) Desired features for data integration platforms in 2012, such as network views of data dependencies and integration with help desk systems.
3) How counterparty risk in banking can be managed through actionable BI solutions that aggregate data from multiple sources, monitor risk factors and exposures, and provide alerts and reporting.
Mistakeproofing the design of construction processes.pdflynelerin1
This document summarizes a study that investigated how mistakeproofing principles can be applied to improve safety and quality in construction processes. It defines mistakeproofing as methods used to design processes in a way that makes it difficult or impossible to make mistakes. The study cataloged 30 examples of mistakeproofing in construction based on its principles and TRIZ (Theory of Inventive Problem Solving) principles. The examples aimed to illustrate how understanding and applying mistakeproofing and TRIZ can help the construction industry avoid errors and improve performance, as has been done in other industries.
This document provides guidance on integrating forensic techniques into incident response. It discusses establishing a forensic capability within an organization, including defining roles and responsibilities, developing policies and procedures, and preparing tools and resources. It also describes the forensic process of collecting, examining, analyzing and reporting on data from various sources, such as files, operating systems, network traffic and applications. The goal is to efficiently and effectively use forensic analysis to understand security incidents and improve an organization's security posture. Legal and technical considerations are also addressed throughout.
National Cybersecurity Talent Workforce Assessment Report of the Philippines.pdfRyan Frunnile
The United States Agency for International Development (USAID) recently released a report attributing the shortage of cybersecurity talent in the Philippines to the lack of career pathways for practitioners, low salary for locally employed specialists particularly in the public sector, and deficiency in current government frameworks for cybersecurity roles and responsibilities. The report also highlights several key recommendations to jumpstart cybersecurity talent development across the government, academia, and industry to bolster the country’s cybersecurity posture and competitiveness in the information, communications, and technology (ICT) industry.
EVOLVING RANSOMWARE ATTACKS ON HEALTHCARE PROVIDERSAyed Al Qartah
Healthcare is among the industries most vulnerable to cyberattacks. As it continues to evolve rapidly and shifting to digitally enabled healthcare services, cybercriminals seek to exploit the vulnerabilities and security weaknesses that are coupled with these changes. As a result of the technological advancements, the healthcare industry is facing a myriad of highly sophisticated threats such as ransomware. The purpose of this research was to identify the evolving ransomware attacks on healthcare providers, their implications, and recommended methods to mitigate future attacks in such a critical industry. The research involved the use of various sources, including security reports from leading cybersecurity companies, research laboratories, scholarly articles, technical whitepapers, and professional journals. The research found healthcare providers are a prime target for ransomware for multiple reasons, including the
rapidly expanding attack surface, lack of adequate cyber defenses, exploiting the human factor, and the heightened sense of urgency to restore confidential patient data or medical systems.
The research found that ransomware breaches can have serious consequences such as reputation damage, disruption of medical care, undermining of patient safety, data privacy loss, and financial costs. This research examined some notable ransomware attacks in the healthcare industry since 2016 such as Hollywood Presbyterian Medical Center and Virtual Care Provider
Incorporated. The research identified the most common infection methods, the different categories of ransomware, and the lifecycle of ransomware infection. The research recommended that healthcare providers should implement multiple layers of cyber defenses and social engineering awareness program to mitigate ransomware attacks.
The Career Guide to the Safety Profession provides an overview of careers in the safety profession. It is published by the American Society of Safety Engineers Foundation and the Board of Certified Safety Professionals to inform students about educational requirements, roles, specializations, and outlook within the field. The guide details the types of work safety professionals perform, such as hazard identification and mitigation, regulatory compliance, health hazard control, and environmental protection. It aims to explain the skills and qualifications needed for a successful career in occupational safety.
Building an Information Technology Security Awareness an.docxrichardnorman90310
Building an Information
Technology Security Awareness
and Training Program
Mark Wilson and Joan Hash
NIST Special Publication 800-50
C O M P U T E R S E C U R I T Y
Computer Security Division
Information Technology Laboratory
National Institute of Standards and Technology
Gaithersburg, MD 20899-8933
October 2003
U.S. Department of Commerce
Donald L. Evans, Secretary
Technology Administration
Phillip J. Bond, Under Secretary for Technology
National Institute of Standards and Technology
Arden L. Bement, Jr., Director
Reports on Computer Systems Technology
The Information Technology Laboratory (ITL) at the National Institute of Standards and Technology
(NIST) promotes the U.S. economy and public welfare by providing technical leadership for the Nation’s
measurement and standards infrastructure. ITL develops tests, test methods, reference data, proof of
concept implementations, and technical analyses to advance the development and productive use of
information technology. ITL’s responsibilities include the development of technical, physical,
administrative, and management standards and guidelines for the cost-effective security and privacy of
sensitive unclassified information in Federal computer systems. This Special Publication 800-series
reports on ITL’s research, guidance, and outreach efforts in computer security, and its collaborative
activities with industry, government, and academic organizations.
U.S. GOVERNMENT PRINTING OFFICE
WASHINGTON: 2003
For sale by the Superintendent of Documents, U.S. Government Printing Office
Internet: bookstore.gpo.gov — Phone: (202) 512-1800 — Fax: (202) 512-2250
Mail: Stop SSOP, Washington, DC 20402-0001
NIST Special Publication 800-50
Authority
This document has been developed by the National Institute of Standards and Technology (NIST) in
furtherance of its statutory responsibilities under the Federal Information Security Management Act
(FISMA) of 2002, Public Law 107-347.
NIST is responsible for developing standards and guidelines, including minimum requirements, for
providing adequate information security for all agency operations and assets, but such standards and
guidelines shall not apply to national security systems. This guideline is consistent with the requirements
of the Office of Management and Budget (OMB) Circular A-130, Section 8b(3), Securing Agency
Information Systems, as analyzed in A-130, Appendix IV: Analysis of Key Sections. Supplemental
information is provided A-130, Appendix III.
This guideline has been prepared for use by federal agencies. It may be used by nongovernmental
organizations on a voluntary basis and is not subject to copyright. (Attribution would be appreciated by
NIST.)
Nothing in this document should be taken to contradict standards and guidelines made mandatory and
binding on federal agencies by the Secretary of Commerce under statutory author.
Florida International UniversityFIU Digital CommonsFIU E.docxvoversbyobersby
Florida International University
FIU Digital Commons
FIU Electronic Theses and Dissertations University Graduate School
2-27-2014
U.S. Construction Worker Fall Accidents: Their
Causes And Influential Factors
Sohaib Siddiqui
[email protected]
Follow this and additional works at: http://digitalcommons.fiu.edu/etd
This work is brought to you for free and open access by the University Graduate School at FIU Digital Commons. It has been accepted for inclusion in
FIU Electronic Theses and Dissertations by an authorized administrator of FIU Digital Commons. For more information, please contact [email protected]
Recommended Citation
Siddiqui, Sohaib, "U.S. Construction Worker Fall Accidents: Their Causes And Influential Factors" (2014). FIU Electronic Theses and
Dissertations. Paper 1157.
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FLORIDA INTERNATIONAL UNIVERSITY
Miami, Florida
U.S. CONSTRUCTION WORKER FALL ACCIDENTS: THEIR CAUSES AND
INFLUENTIAL FACTORS
A thesis submitted in partial fulfillment of
the requirements for the degree of
MASTER OF SCIENCE
in
CONSTRUCTION MANAGEMENT
by
Sohaib Siddiqui
2014
ii
To: Dean Amir Mirmiran
College of Engineering and Computing
This thesis, written by Sohaib Siddiqui, and entitled U.S. Construction Worker Fall
Accidents: Their Causes and Influential Factors, having been approved in respect to style
and intellectual content, is referred to you for judgment.
We have read this thesis and recommend that it be approved.
Irtishad Ahmad
Jose Faria
Youngcheol Kang, Major Professor
Date of Defense: February 27, 2014
The thesis of Sohaib Siddiqui is approved.
Dean Amir Mirmiran
College of Engineering and Computing
Dean Lakshmi N. Reddi
University Graduate School
Florida International University, 2014
iii
DEDICATION
This thesis is dedicated to my family especially my mother, my father, my wife and
my daughter; as without their unwavering support and blessings, this academic milestone
would not have been possible to achieve. To Dr. Youngcheol Kang, as without his
remarkable direction and support this thesis would not have b ...
AIDSTAR-One Assessment of Infection Prevention and Patient Safety Commodities...AIDSTAROne
In Ethiopia, ensuring a sufficient and sustainable supply of infection prevention and patient safety (IPPS) commodities is an important strategy to combat the high risk of transmission of health care–associated infections. However, there is a lack of awareness on the proper utilization of IPPS commodities by health care workers, and a lack of accurate data on the quantity of essential IPPS commodities needed by the health care system to adequately protect workers, patients, and the community from health care-associated infections. This assessment used a consultative approach to develop a national standardized and prioritized list of IPPS commodities for all levels of health care facilities, and quantified the annual need of IPPS commodities for the four levels of health care facilities in Ethiopia. This report summarizes the findings of the assessment.
www.aidstar-one.com/focus_areas/prevention/resources/reports/ethiopia_ipps
This document discusses the challenges healthcare organizations face in securing protected health information and complying with regulations in light of increased automation and electronic records adoption. It outlines various security laws and regulations for healthcare including HITECH, which strengthens HIPAA and creates data breach notification requirements. The document provides an overview of best practices for healthcare organizations to assess security risks, prevent data loss, meet regulatory requirements, and secure systems while maintaining patient care.
Approved for public release; distribution is unlimited. Sy.docxjewisonantone
This document provides an overview of innovative uses of social media in emergency management based on case studies and literature review. It finds that public safety organizations are increasingly using social media to engage with communities before, during, and after emergencies to share timely information and gain situational awareness. The case studies highlight how government agencies and non-profits collaborated with communities through social media during recent natural disasters like hurricanes, earthquakes, floods and wildfires to coordinate responses and distribute updates. The document also outlines best practices for social media implementation identified in literature, including developing strategic and policy frameworks, actively monitoring content, and using mapping tools to provide visual context.
Approved for public release; distribution is unlimited. Sy.docxfestockton
Approved for public release; distribution is unlimited.
System Assessment and Validation for Emergency Responders (SAVER)
Innovative Uses of Social Media in
Emergency Management
September 2013
Prepared by Space and Naval Warfare Systems Center Atlantic
The Innovative Uses of Social Media in Emergency Management report was funded under
Interagency Agreement No. HSHQDC-07-X-00467 from the U.S. Department of
Homeland Security, Science and Technology Directorate.
The views and opinions of authors expressed herein do not necessarily reflect those of the
U.S. Government.
Reference herein to any specific commercial products, processes, or services by trade
name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its
endorsement, recommendation, or favoring by the U.S. Government.
The information and statements contained herein shall not be used for the purposes of
advertising, nor to imply the endorsement or recommendation of the U.S. Government.
With respect to documentation contained herein, neither the U.S. Government nor any of
its employees make any warranty, express or implied, including but not limited to the
warranties of merchantability and fitness for a particular purpose. Further, neither the
U.S. Government nor any of its employees assume any legal liability or responsibility for
the accuracy, completeness, or usefulness of any information, apparatus, product, or
process disclosed; nor do they represent that its use would not infringe privately owned
rights.
Cover images are courtesy of Federal Emergency Management Agency (FEMA) News
Photos.
Approved for public release; distribution is unlimited.
i
FOREWORD
The U.S. Department of Homeland Security (DHS) established the System Assessment and
Validation for Emergency Responders (SAVER) Program to assist emergency responders
making procurement decisions. Located within the Science and Technology Directorate (S&T)
of DHS, the SAVER Program conducts objective assessments and validations on commercial
equipment and systems and provides those results along with other relevant equipment
information to the emergency response community in an operationally useful form. SAVER
provides information on equipment that falls within the categories listed in the DHS Authorized
Equipment List (AEL). The SAVER Program mission includes:
Conducting impartial, practitioner-relevant, operationally oriented assessments and
validations of emergency responder equipment; and
Providing information, in the form of knowledge products, that enables decision-makers
and responders to better select, procure, use, and maintain emergency responder
equipment.
Information provided by the SAVER Program will be shared nationally with the responder
community, providing a life- and cost-saving asset to DHS, as well as to Federal, state, and local
responders.
The SAVER Program is supported by a network of Technical Agents who perf ...
This document provides guidance for building an effective IT security awareness and training program as required by FISMA and OMB Circular A-130. It discusses key roles and responsibilities, components of an awareness and training program, and a lifecycle approach for designing, developing, implementing and evaluating such a program. The goal is to ensure all IT users understand security policies and responsibilities to protect systems and data.
Denver, CO – June 23, 2014 - The Colorado BioScience Association (CBSA) is releasing its annual edition of Bioscience Colorado, this week, at the BIO International Convention in San Diego, CA. The announcement is made by CBSA President & CEO April Giles, who says, “In this issue we are looking at the intersection of people and data, and how it benefits human health. Big Data is now playing a critical role within the bioscience industry—from the personal devices on our wrists monitoring our bodies, to electronic medical records housing our health histories—data is valuable.”
A Worker Dialogue: Improving Health, Safety and Security at the U.S. Departme...The Collaboration Project
During the summer of 2010, the Department of Energy Office of Health, Safety and Security (HSS) partnered with the National Academy of Public Administration to host an online dialogue to solicit ideas from front line union workers at DOE sites on how to improve worker safety across the DOE complex. Based on the results of the Dialogue, an expert Panel of the National Academy identified several themes that emerged from workers’ suggestions and offered recommendations for HSS in following up on the issues raised as well as continuing to build its capacity for employee engagement.
The document summarizes the key findings of the 2011 Global Information Security Workforce Study conducted by Frost & Sullivan. Some of the main points from the summary include:
1) Application vulnerabilities were reported as the number one threat to organizations, with over 20% of security professionals reporting involvement in software development.
2) Mobile devices were the second highest security concern, despite most professionals having policies and tools in place to defend against mobile threats.
3) A skills gap exists as new technologies like cloud computing and social media are being adopted without sufficient security training for professionals. Over 70% needed new skills for cloud security.
4) The information security workforce is projected to grow significantly from 2.28 million in 2010
The document discusses the potential applications of 3D printing in the medical field. It describes how 3D printing can be used to create customized prosthetics at lower costs, potentially making them accessible to more people. Researchers are also exploring using 3D printing to repair and potentially replace organs by directly printing cells or tissue scaffolds. 3D printing is further being studied for applications such as printing skin grafts or surgical models to assist with operations. The technology shows promise for revolutionizing various aspects of the medical industry.
This document summarizes research on using dissipation factor (tan δ) testing to assess the condition of power cables. It describes how tan δ is measured, factors that affect its accuracy, criteria for evaluating results, and insights from analyzing a database of over 2,000 cable systems. Key findings include developing diagnostic levels based on combinations of tan δ features, observing differences between cable types, and improving assessments by increasing the data set size. The research aims to establish the most reliable approach for utilities to evaluate cable condition using tan δ testing.
Sp800 55 Rev1 Performance Measurement Guide For Information SecurityJoao Couto
This document provides guidance on developing and implementing performance measures to assess the effectiveness of information security controls and programs. It describes a process for establishing measures that are quantifiable and can be used to track performance over time. The measures developed should indicate how well security policies are implemented, how efficiently security controls operate, and the impact of any security issues. This will help organizations comply with laws like FISMA and use security metrics to improve practices and allocate resources. The guidance can be applied at the individual system or enterprise program level.
The document provides background information on the informal sector economy. It discusses how the informal sector economy accounts for around 50% of GDP in emerging economies and examines whether it should be formalized. The research aims to explore the advantages and disadvantages of both the informal sector economy and formalization to determine the most sustainable strategy for managing informal enterprises. It conducted interviews with experts and entrepreneurs in the informal sector.
South Dakota State University degree offer diploma Transcriptynfqplhm
办理美国SDSU毕业证书制作南达科他州立大学假文凭定制Q微168899991做SDSU留信网教留服认证海牙认证改SDSU成绩单GPA做SDSU假学位证假文凭高仿毕业证GRE代考如何申请南达科他州立大学South Dakota State University degree offer diploma Transcript
Abhay Bhutada, the Managing Director of Poonawalla Fincorp Limited, is an accomplished leader with over 15 years of experience in commercial and retail lending. A Qualified Chartered Accountant, he has been pivotal in leveraging technology to enhance financial services. Starting his career at Bank of India, he later founded TAB Capital Limited and co-founded Poonawalla Finance Private Limited, emphasizing digital lending. Under his leadership, Poonawalla Fincorp achieved a 'AAA' credit rating, integrating acquisitions and emphasizing corporate governance. Actively involved in industry forums and CSR initiatives, Abhay has been recognized with awards like "Young Entrepreneur of India 2017" and "40 under 40 Most Influential Leader for 2020-21." Personally, he values mindfulness, enjoys gardening, yoga, and sees every day as an opportunity for growth and improvement.
Vicinity Jobs’ data includes more than three million 2023 OJPs and thousands of skills. Most skills appear in less than 0.02% of job postings, so most postings rely on a small subset of commonly used terms, like teamwork.
Laura Adkins-Hackett, Economist, LMIC, and Sukriti Trehan, Data Scientist, LMIC, presented their research exploring trends in the skills listed in OJPs to develop a deeper understanding of in-demand skills. This research project uses pointwise mutual information and other methods to extract more information about common skills from the relationships between skills, occupations and regions.
Economic Risk Factor Update: June 2024 [SlideShare]Commonwealth
May’s reports showed signs of continued economic growth, said Sam Millette, director, fixed income, in his latest Economic Risk Factor Update.
For more market updates, subscribe to The Independent Market Observer at https://blog.commonwealth.com/independent-market-observer.
A toxic combination of 15 years of low growth, and four decades of high inequality, has left Britain poorer and falling behind its peers. Productivity growth is weak and public investment is low, while wages today are no higher than they were before the financial crisis. Britain needs a new economic strategy to lift itself out of stagnation.
Scotland is in many ways a microcosm of this challenge. It has become a hub for creative industries, is home to several world-class universities and a thriving community of businesses – strengths that need to be harness and leveraged. But it also has high levels of deprivation, with homelessness reaching a record high and nearly half a million people living in very deep poverty last year. Scotland won’t be truly thriving unless it finds ways to ensure that all its inhabitants benefit from growth and investment. This is the central challenge facing policy makers both in Holyrood and Westminster.
What should a new national economic strategy for Scotland include? What would the pursuit of stronger economic growth mean for local, national and UK-wide policy makers? How will economic change affect the jobs we do, the places we live and the businesses we work for? And what are the prospects for cities like Glasgow, and nations like Scotland, in rising to these challenges?
University of North Carolina at Charlotte degree offer diploma Transcripttscdzuip
办理美国UNCC毕业证书制作北卡大学夏洛特分校假文凭定制Q微168899991做UNCC留信网教留服认证海牙认证改UNCC成绩单GPA做UNCC假学位证假文凭高仿毕业证GRE代考如何申请北卡罗莱纳大学夏洛特分校University of North Carolina at Charlotte degree offer diploma Transcript
Fabular Frames and the Four Ratio ProblemMajid Iqbal
Digital, interactive art showing the struggle of a society in providing for its present population while also saving planetary resources for future generations. Spread across several frames, the art is actually the rendering of real and speculative data. The stereographic projections change shape in response to prompts and provocations. Visitors interact with the model through speculative statements about how to increase savings across communities, regions, ecosystems and environments. Their fabulations combined with random noise, i.e. factors beyond control, have a dramatic effect on the societal transition. Things get better. Things get worse. The aim is to give visitors a new grasp and feel of the ongoing struggles in democracies around the world.
Stunning art in the small multiples format brings out the spatiotemporal nature of societal transitions, against backdrop issues such as energy, housing, waste, farmland and forest. In each frame we see hopeful and frightful interplays between spending and saving. Problems emerge when one of the two parts of the existential anaglyph rapidly shrinks like Arctic ice, as factors cross thresholds. Ecological wealth and intergenerational equity areFour at stake. Not enough spending could mean economic stress, social unrest and political conflict. Not enough saving and there will be climate breakdown and ‘bankruptcy’. So where does speculative design start and the gambling and betting end? Behind each fabular frame is a four ratio problem. Each ratio reflects the level of sacrifice and self-restraint a society is willing to accept, against promises of prosperity and freedom. Some values seem to stabilise a frame while others cause collapse. Get the ratios right and we can have it all. Get them wrong and things get more desperate.
The Universal Account Number (UAN) by EPFO centralizes multiple PF accounts, simplifying management for Indian employees. It streamlines PF transfers, withdrawals, and KYC updates, providing transparency and reducing employer dependency. Despite challenges like digital literacy and internet access, UAN is vital for financial empowerment and efficient provident fund management in today's digital age.
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Falcon stands out as a top-tier P2P Invoice Discounting platform in India, bridging esteemed blue-chip companies and eager investors. Our goal is to transform the investment landscape in India by establishing a comprehensive destination for borrowers and investors with diverse profiles and needs, all while minimizing risk. What sets Falcon apart is the elimination of intermediaries such as commercial banks and depository institutions, allowing investors to enjoy higher yields.
1. Predictive Analytics
White Paper
Charles Nyce,PhD,CPCU,API
Senior Director of Knowledge Resources
American Institute for CPCU/
Insurance Institute of America
CopyrightAICPCU/IIAandsubjecttoallcopyrightlaws.
3. Predictive Analytics
White Paper
Charles Nyce,PhD,CPCU,API
Senior Director of Knowledge Resources
American Institute for CPCU • Insurance Institute of America
720 Providence Road • Suite 100 • Malvern,PA 19355-3433
Phone (610) 644-2100,ext. 7245 • Fax (610) 644-7387
www.aicpcu.org
CopyrightAICPCU/IIAandsubjecttoallcopyrightlaws.
5. iii
Contents
Introduction........................................................................................................................................................................... 1
Overview of Predictive Analytics........................................................................................................................................ 1
Drivers of Insurers’Use of Predictive Analytics................................................................................................................ 2
Technological Advances.........................................................................................................................................2
Data Availability.....................................................................................................................................................2
Insurers’ Desire for Growth In Slow-growth Markets.............................................................................................2
Insurers’ Search for Competitive Advantage..........................................................................................................3
Insurers’Use of Predictive Analytics.................................................................................................................................. 4
Marketing................................................................................................................................................................4
Hit Ratio.............................................................................................................................................................4
Retention Ratio..................................................................................................................................................5
Underwriting...........................................................................................................................................................5
Claims......................................................................................................................................................................5
Identifying Potentially Fraudulent Claims.........................................................................................................5
Prioritizing Claims..............................................................................................................................................7
The Predictive Analytic Process.......................................................................................................................................... 7
Data Mining.............................................................................................................................................................9
Model Development................................................................................................................................................9
Regression Models..............................................................................................................................................9
Advanced Models.............................................................................................................................................11
Model Validation...................................................................................................................................................11
Predictive Analytics’Advantages for Insurers................................................................................................................11
Predictive Analytics’Disadvantages for Insurers..........................................................................................................12
Frequently Asked Questions Regarding Widespread Insurer .
Use of Predictive Analytic Techniques.............................................................................................................................14
CopyrightAICPCU/IIAandsubjecttoallcopyrightlaws.
7. Foreword
The American Institute for Chartered Property Casualty Underwriters and the
Insurance Institute of America (the Institutes) are independent, not-for-profit
organizations committed to expanding the knowledge of professionals in risk
management, insurance, financial services, and related fields through education
and research.
In accordance with our belief that professionalism is grounded in education,
experience, and ethical behavior, the Institutes provide a wide range of
educational programs designed to meet the needs of individuals working in
risk management and property-casualty insurance. The American Institute
offers the Chartered Property Casualty Underwriter (CPCU®
) professional
designation, designed to provide a broad understanding of the property-
casualty insurance industry. CPCU students may select either a commercial
or a personal risk management and insurance focus, depending on their
professional needs.
The Insurance Institute of America (IIA) offers designations and certificate
programs in a variety of disciplines, including the following:
• Claims
• Commercial underwriting
• Fidelity and surety bonding
• General insurance
• Insurance accounting and finance
• Insurance information technology
• Insurance production and
agency management
• Insurance regulation and
compliance
• Management
• Marine insurance
• Personal insurance
• Premium auditing
• Quality insurance services
• Reinsurance
• Risk management
• Surplus lines
You may choose to take a single course to fill a knowledge gap, complete
a program leading to a designation, or take multiple courses and programs
throughout your career. No matter which approach you choose, you will
gain practical knowledge and skills that will contribute to your professional
growth and enhance your education and qualifications in the expanding
insurance market. In addition, many CPCU and IIA courses qualify for credits
toward certain associate, bachelor’s, and master’s degrees at several prestigious
colleges and universities, and all CPCU and IIA courses carry college credit
recommendations from the American Council on Education.
CopyrightAICPCU/IIAandsubjecttoallcopyrightlaws.
8. vi
The American Institute for CPCU was founded in 1942 through a collab-
orative effort between industry professionals and academics, led by faculty
members at The Wharton School of the University of Pennsylvania. In 1953,
the American Institute for CPCU merged with the Insurance Institute of
America, which was founded in 1909 and which remains the oldest continu-
ously functioning national organization offering educational programs for the
property-casualty insurance business.
The Insurance Research Council (IRC), founded in 1977, helps the Institutes
fulfill the research aspect of their mission. A division of the Institutes, the
IRC is supported by industry members. This not-for-profit research organiza-
tion examines public policy issues of interest to property-casualty insurers,
insurance customers, and the general public. IRC research reports are distrib-
uted widely to insurance-related organizations, public policy authorities, and
the media.
The Institutes strive to provide current, relevant educational programs in
formats and delivery methods that meet the needs of insurance professionals
and the organizations that employ them. Institute textbooks are an essential
component of the education we provide. Each book is designed to clearly and
concisely provide the practical knowledge and skills you need to enhance your
job performance and career. The content is developed by the Institutes in
collaboration with risk management and insurance professionals and members
of the academic community. We welcome comments from our students and
course leaders; your feedback helps us continue to improve the quality of our
study materials.
Peter L. Miller, CPCU
President and CEO
American Institute for CPCU
Insurance Institute of America
CopyrightAICPCU/IIAandsubjecttoallcopyrightlaws.
9. Predictive Analytics
INTRODUCTION
While accurately forecasting factors such as operations, budgets, supplies, or
product demand is crucial to any organization’s success, insurance organizations
are particularly reliant on predicting future activities. An insurer’s ability to fore-
cast a policy’s ultimate cost determines how accurately it prices its product and,
in turn, the extent to which it can avoid adverse selection.
Insurance has always relied on forecasting. Initially, insurers simply guessed
appropriate premiums. Subsequently, they determined premiums by analyzing
a single factor, such as the age of an insured building or the piloting history of
an insured ship’s captain (univariate analysis). As insurance operations became
more technologically advanced, multiple factors such as the age of the insured
building, its type of construction, its usage, and so forth (multivariate analysis)
were used to determine an appropriate premium. Today, insurers use techniques
known as predictive analytics to determine additional information such as credit
scores or local economic conditions that may be relevant or correlated with a
potential insurance outcome.
The use of predictive analytics has quickly become an insurance industry best
practice. Insurers use predictive analytic techniques to target potential clients, to
determine more accurate pricing, and to identify potentially fraudulent claims.
This white paper discusses the foundations of predictive analytics, the drivers of
its growth, its uses in the insurance industry, the implications of its widespread
use, and some of its technical aspects.
OVERVIEW OF PREDICTIVE ANALYTICS
Predictive analytics is a broad term describing a variety of statistical and analyti-
cal techniques used to develop models that predict future events or behaviors.
The form of these predictive models varies, depending on the behavior or event
that they are predicting. Most predictive models generate a score (a credit score,
for example), with a higher score indicating a higher likelihood of the given
behavior or event occurring.
Predictive Models:Credit Score
The most prevalent examples of predictive models are those used by the three credit bureaus
(Experian,Equifax,andTransUnion) to develop credit scores for individuals.Each credit bureau uses
a variety of information about an individual (income,credit history,outstanding loan balances,and
so forth) to develop a credit score that predicts the likelihood that he or she will repay current and
future debts.The higher the credit score,the more likely the individual is to pay his/her debt.
Data mining is a component of predictive analytics that entails analysis of data
to identify trends, patterns, or relationships among the data. This information
can then be used to develop a predictive model. Predictive analytics, along with
most predictive models and data mining techniques, rely on increasingly sophis-
ticated statistical methods, including multivariate analysis techniques such as
advanced regression or time-series models. Such techniques enable organizations
to determine trends and relationships that may not be readily apparent, but still
enable it to better predict future events or behaviors.
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10. Predictive Analytics
DRIVERS OF INSURERS’USE OF PREDICTIVE ANALYTICS
Though businesses have employed predictive analytics techniques for many
years, several drivers have increased their prevalence throughout the insurance
industry. These drivers include the following:
Insurance Industry Drivers of Predictive Analytics
• Technological advances
• Data availability
• Insurers’desire for growth in slow markets
• Insurers’search for competitive advantage
Technological Advances
The statistical techniques used in predictive analytics are computationally
intensive. Depending on the amount of data they use, some require perform-
ing thousands or millions of calculations. Advances in computer hardware and
software design have yielded software packages that quickly perform such calcu-
lations, allowing insurers to efficiently analyze the data that produce and validate
their predictive models.
Data Availability
The validity of any predictive model depends on the quality and quantity of
data available to develop it. While most insurers today have a sufficient amount
of data (quantity) to develop their predictive models, many store policyholder
information on legacy systems that may not be compatible with systems run-
ning predictive analytics software. Converting data on these legacy systems to a
usable format can be time consuming and costly.
In addition to the insurers’ proprietary data, there are numerous third party
sources of data that insurers can use to develop predictive models. These sources
include rating bureaus, regulators, advisory organizations, rating agencies, predic-
tive modeling companies, and other data gathering organizations.
Insurers’Desire for Growth in Slow-Growth Markets
Although the property-casualty insurance industry generates more than $400 bil-
lion in premiums annually, premiums have grown at a rate of less than 5 percent
per year over the last ten years.1
In many lines of insurance, including personal
lines (where predictive analytics are used most frequently), the growth rate has
been substantially lower. This slow growth rate has driven insurers to look for
other ways to expand their market share. Insurers can use predictive analytics
to develop more accurate pricing and to improve how they target their services.
Thus, insurers that use predictive analytics can claim market share from their
less efficient competitors.
1
Source: Author’s calculations, Insurance Information Institute Web site, www.III.org.
CopyrightAICPCU/IIAandsubjecttoallcopyrightlaws.
11. Predictive Analytics
Insurers’Search for Competitive Advantage
The final driver of the use of predictive analytics, insurers’ search for competitive
advantage, is related to insurers’ desire for growth because their desire for market
share leads them to seek advantages over their competitors. The use of predic-
tive analytics may provide insurers with information about applicants that their
competitors do not possess. If an insurer’s predictive model’s rating or score for
applicants accurately forecasts behavior, the insurer can more efficiently define
the target market, more accurately develop pricing, and more efficiently handle
claims, all of which provide it with a competitive advantage over competitors
who do not use predictive analytics. As more insurers use predictive analytics,
those not doing so will be increasingly exposed to adverse selection because their
market will be limited to a subsection of the general population that has worse-
than-average loss ratios.
Adverse Selection
Consumers with the greatest probability of loss are those most likely to purchase insurance.This
phenomenon is known as adverse selection.
Appropriate insurance pricing requires that the insurer gather information about the applicant
sufficient to adequately assess and price a particular policy.Although much information about
an applicant is available from the applicant and other sources,it can be expensive for insurers to
collect.After taking into account all of the information that can be collected cost-effectively,a
portion of the information about the applicant remains unknown to the insurer.In economic terms,
this is called information asymmetry and it occurs when one party has information that is relevant
to the transaction (the applicant) that the other party (the insurer) does not have.Although this
information is important to insurers and would enable them to appropriately price their insurance
products,the benefits to the insurer of appropriate pricing do not outweigh the costs of obtaining
the additional information.
When an insurer charges an average rate because it cannot differentiate between a low risk and
a high risk,high-risk individuals have an incentive to buy the insurance because the premium is
too low.Conversely,low-risk individuals do not want to buy the insurance because the premium
is too high.This results in adverse selection.Therefore,despite offering the average rate,the group
of people the insurer has insured is not an average group—it is worse than average because of
adverse selection.This group of insureds would tend to have more accidents and higher claims
than the average group because they are poor risks.Avoiding adverse selection is one of the main
functions of underwriting.
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12. Predictive Analytics
INSURERS’USE OF PREDICTIVE ANALYTICS
The following discussion of applications of predictive analytics focuses on the
three core insurer functions—marketing, underwriting, and claims. While policy
pricing may be considered an actuarial function, it is included in the discussion
of underwriting.
Insurance Industry Use of Predictive Analytics
Marketing Property-casualty insurers can use predictive analytics to analyze the purchas-
ing patterns of insurance customers.This information can be used to increase the
marketing function’s hitratio and retentionratio.
Underwriting Insurers can use predictive analytics to filter out applicants who do not meet a
pre-determined model score.This type of screening can greatly increase an insurer’s
efficiency by reducing the employee hours it may have spent researching and ana-
lyzing an applicant who ultimately is not a desired insured.If an applicant’s model
score is sufficient for consideration,then the model score can be used as a rating
mechanism on which the insurer can base a variety of price/product points.
Claims Insurers can use predictive analytics to help identify potentially fraudulent claims.It
also can be used to score claims based on the likely size of the settlement,enabling
an insurer to more efficiently allocate resources to higher priority claims.
Marketing
Insurance marketing has often relied on referrals and other traditional marketing
approaches. Predictive modeling in insurance marketing represents a revolu-
tionary approach to what has commonly been perceived as a relationship-based
business.
Predictive analytics is used in the marketing of many products and services.
Financial services organizations use predictive analytics to identify potential
customers for mortgages, annuities, loans, and investments. Property-casu-
alty insurers can use predictive analytics to analyze the purchasing patterns of
insurance customers. This information can be used to increase the marketing
function’s hit ratio and retention ratio.
Hit Ratio
Hit ratio is a measure of how often the marketing function generates a sale
for each contact made with a potential customer. If an agent makes one sale
for every ten potential clients, his or her hit ratio is one in ten (10 percent).
Predictive analytics used to analyze purchasing patterns may allow the agent to
focus on customers more likely to buy, thereby increasing his or her hit ratio.
For example, if predictive analytics identifies the two customers out of every ten
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13. Predictive Analytics
potential customers who are least likely to purchase a policy, the elimination of
those potential customers from an agent’s sales agenda will raise his or her hit
ratio to one in eight (12.5 percent).
Retention Ratio
Similarly, an insurer can use predictive analytics to attempt to retain the busi-
ness of customers who are more likely to purchase insurance from another
provider. If it succeeds, the insurer will increase its retention ratio, which is a
measure of how many insureds renew policies relative to the total number of
insureds. For example, if nine out of ten insureds renew their insurance policies,
the insured’s retention ratio is 90 percent.
Underwriting
The use of predictive analytics in underwriting is more evolutionary than revolu-
tionary. Underwriting has always attempted to accurately predict future losses and
price the products that protect against those losses. Predictive analytics represents
the next generation of underwriting tools available to achieve those goals.
Predictive models can be used in expert underwriting systems to remove the
human error factor from the underwriting process by streamlining the “normal”
underwriting cases and only referring the “exceptions” to the human underwrit-
ers. Insurers can use predictive analytics to filter out applicants who do not meet
a pre-determined model score. This type of screening can greatly increase an
insurer’s efficiency by reducing the employee hours it may have spent research-
ing and analyzing an applicant who ultimately is not a desired insured. If an
applicant’s model score is sufficient for consideration, than the model score can
be used as a rating mechanism on which the insurer can base a variety of price/
product points.
Claims
Predictive analytics is more of a revolutionary concept in claims handling than it
is in underwriting. Insurers can use predictive analytics to help identify poten-
tially fraudulent claims. It also can be used to score claims based on the likely
size of the settlement, enabling an insurer to more efficiently allocate resources
to higher priority claims.
Identifying Potentially Fraudulent Claims
Insurers have struggled for years to develop methods to identify potentially fraud-
ulent claims. While the Insurance Information Institute estimates that insurance
fraud costs property-casualty insurers over $30 billion annually, it is difficult
to estimate the actual percentage of all claims that are fraudulent. Fraud can
take many forms, from staged accidents to the padding or building up of claims
(inflating the value of the claim) for accidents that have already occurred. The
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14. Predictive Analytics
Property-casualty insurers traditionally have had difficulty identifying the rela-
tively small number of fraudulent claims (the needle) made among the millions
of claims filed every year (the haystack). Predictive analytics can help insur-
ers more accurately determine claims that need additional review for fraud by
increasing the likelihood of discovering fraudulent claims and helping it to refine
the claims marked for review. This is known as limiting the occurrence of type I
and type II errors. A type I error occurs when a legitimate claim is identified as
possibly fraudulent. A type II error is the failure to identify a fraudulent claim
and paying it as if it were legitimate, illustrated as follows:
BI PIP MP UM UIM
9%
18%
5%
12%
4%
9%
8%
14%
7%
16%
Claims with the appearance of fraud Claims with the appearance of buildup
Claims with the appearance of both fraud and buildup are included in each group.
PercentageofAutoInjuryInsuranceClaimsWiththeAppearance
ofFraudorBuildup
Fraudulent
Claims
All Claims
Claims
Identified as
Fraudulent
Fraudulent Claims
Correctly Identified
Type I Errors
Type II Errors
Insurance Research Council’s study, Fraud and Buildup in Auto Injury Insurance
Claims: 2004 Edition, estimates that nearly one in five auto injury insurance
claims may have the appearance of fraud, illustrated as follows:
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15. Predictive Analytics
Both types of errors can be costly. Identifying legitimate claims as fraudulent may
anger policyholders and result in litigation or accusations of bad faith in claims
practices. Failing to identify fraudulent claims results in higher claims costs and
therefore higher premiums for all insureds. Any tools that can aid in the accurate
identification of fraudulent claims reduces both types of errors and significantly
improves the claims process.
Prioritizing Claims
A second use of predictive analytics in the claims process is the prioritizing of
claims for handling. Predictive analytics can help identify claims at an early
stage that are likely to be settled for higher values. These higher-value claims
can then be identified as high-priority claims. More accurately identifying high-
priority claims helps the claims function operate more efficiently. High-priority
claims can then be handled internally, while the handling of lower-priority
claims can be outsourced.
THE PREDICTIVE ANALYTIC PROCESS
When using predictive analytics, an insurer starts by aggregating and “cleansing”
its data for use in the analytics software. Cleansing entails scouring records to
identify those with missing or incomplete data. Records with missing or incom-
plete data can have an impact on the accuracy of the predictive model. These
records must be completed and/or corrected so that the ultimate predictive
model is as accurate as possible.
For example, an insurer developing a predictive model for auto insurance claims
would start with its own marketing, underwriting, and claims records for auto
policies it has sold. Some of the records may have missing information, such
as the age, sex, or marital status of the insured, or may not contain the com-
plete details of the claim (such as not noting whether a police report was filed
or subrogation efforts were made). For some insurers, determining this missing
information may involve a long and costly process, Multiple legacy systems that
may have accumulated through an insurer’s mergers and acquisitions add to the
difficulty of converting the data to a usable format.
Once the data have been aggregated and cleansed, sound statistical practices dic-
tate that they be divided into an in-sample group that will be used to develop the
predictive model and an out-of-sample group that will be used to test the model.
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17. Predictive Analytics
Data Mining
Data mining is the analysis of data to identify underlying trends, patterns, or
relationships. It is a necessary first step in predictive analytics, because the data
that the mining process identifies as relevant can then be used to develop the
predictive model. One can think of data mining as gathering knowledge about
relationships, and the resulting predictive analytics model as applying that
knowledge. One distinct advantage to data mining is that it catalogs all relation-
ships (or correlations) that may be found among the data, regardless of what
causes that relationship. For example, data mining may discern a relationship
between age and gray hair, or age and number of auto accidents, but it does not
imply that age causes gray hair or auto accidents.
Model Development
Predictive models can assume many shapes and sizes, depending on their com-
plexity and the application for which they are designed. This section introduces
some of the statistical methods that may be used to develop a predictive model.
Unlike data mining, many of the statistical procedures that are employed in
predictive models search for one specific relationship. This may require, for
example, specifying during model development that age does cause gray hair or
that age may reduce the likelihood of auto accidents (at least up to certain ages).
Regression Models
Regression Predictive Models andTheir Uses
Linear Regression Can be adapted to a wide variety of data types,including time series,cross
sectional,pooled,or panel data
Partial and Stepwise
Regressions
Measures how one independent variable and the dependent variable are
related after determining the effect of all the other independent variables in
the model
Logit or Probit
Regressions
Allow one to predict a discrete outcome (yes or no) from a set of variables
that may be continuous,discrete and/or dichotomous
Regression Splines Allow different regression models to model data over different regions of the
dependent variable
Regression modeling mathematically describes the relationship between the
dependent variable (in predictive models, the variable to be predicted) and
independent, explanatory variables given sample data. Regression implies some
causation, unlike correlation since modelers must specify a relationship before-
hand and then test how well the regression model fits or models the specified
relationship. For example, suppose a regression model was designed to examine
the relationship between an insured’s auto physical damage loss ratio and a few
independent variables such as age, type of car driven, and driving history. The
model may show a negative relationship between age and loss ratio. In other
words, younger drivers have more losses than older drivers. In this case, it’s safe
to assume that being younger causes more losses, not that more losses causes
drivers to be younger.
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18. 10
Predictive Analytics
Regression Basics
Ideally,to explain relationships between variables (such as age and losses in auto),insurers would
examine the entire population (in this case,the entire population of drivers).However,insurers can
only draw data from a sample of the population (such as only the drivers they insure),so they must
do the following:
1. Build the best model they can to determine the“true”relationship between variables.
2. Analyze confidence in the model (mathematical description of its accuracy).
Predictive models may use a variety of regression models. The most basic
regression models are linear regression models such as ordinary least squares
(OLS) regression. Some of the most complex regression models are multivariate
adaptive regression splines called MARSplines.
RegressionTechniques Used in Predictive Models
Linear Regression
In a linear regression model,the dependent variable (the variable the model is attempting to predict
or explain) is a linear function of several explanatory variables.The most common linear regression
models are OLS regressions.Linear regression models can be adapted to a wide variety of data types
such as time series,cross sectional,pooled,or panel data.
Partial or Stepwise Regressions
Partial (or stepwise) regression is a regression procedure in which the modeler does not need to
specify all of the explanatory variables beforehand,but instead allows the regression procedure to
iteratively add variables to the model based on the partial correlation of that variable.For example,
after accounting for the age of the driver,how much does car type affect the probability of a car
accident? Partial correlation measures how one independent variable and the dependent variable
are related after determining the effect of all the other independent variables in the model.
Logit or Probit Regressions
Logit and probit are two examples of a larger class of“generalized linear models.”This broad class
of models includes ordinary regression and ANOVA (analysis of variance),as well as multivariate
statistics such as ANCOVA (analysis of covariance) and loglinear regression.Logit and probit
regressions allow one to predict a discrete outcome (for example,group membership or a fraudulent
claim) from a set of variables that may be continuous,discrete and/or dichotomous.Generally,the
dependent or response variable is dichotomous (for example,will/will not or success/failure).
Regression Splines
Regression splines allow different regression models to model data over different regions of the
dependent variable.For example,assume that for all drivers the probability of auto accidents
ranges from .001 percent to 45 percent for a given year.Perhaps the variables that best model
the probabilities between .001 percent and 5 percent are age,type of vehicle,and location,while
the probabilities between 5 percent and 45 percent are best modeled by type of vehicle,location,
number of moving violations and gender.Regression splines allow the model to be created
piecewise over various portions of the dependent variable’s distribution.
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19. Predictive Analytics
11
Advanced Models
In addition to regression models, more advanced models such as neural networks
may be used to create predictive models. Neural networks are nonlinear statisti-
cal modeling tools. In general, neural networks can handle many more variables
than the regression techniques discussed previously and also address some of the
other limitations associated with regression techniques such as statistical con-
cerns regarding dimensionality.
ModelValidation
To ensure that a predictive model is as accurate as possible, it must be validated
through out-of-sample testing. Out-of-sample testing divides data into in-sample
data (the data used to develop the model) and out-of-sample data (the model
testing window), which includes only data not used. For example, suppose an
insurer has twenty-four months’ worth of data on the frequency of homeowners’
claims. To properly construct and validate a predictive model using the data,
the modeler may choose use the first eighteen months’ worth of data. Once the
model has been developed, data from the final six months could then be used to
validate it.
Sound statistical practices dictate that multiple in-sample and out-of-sample
data windows be used to develop and validate a predictive model. In the home-
owners claim example, the predictive model would not have been properly
validated if a major catastrophe had occurred during the six-month out-of-
sample testing window. Using multiple out-of-sample testing windows would
minimize the influence of such a single event.
PREDICTIVE ANALYTICS’ADVANTAGES FOR INSURERS
If knowledge is power, then the advantages of predictive analytics are clear.
Predictive analytic techniques allow insurers to better understand their data
and how to use it to predict future events. Proper implementation of predic-
tive analytic techniques can improve an insurer’s consistency and efficiency in
marketing, underwriting, and claims services by helping to define target markets,
increasing the number of policy price points, and reducing claims fraud.
Predictive AnalyticTechniques’Advantages for Insurers
• Helps marketing department more precisely identify potential policy sales through analysis of
customer purchasing patterns
• Reduces the employee hours underwriters may have spent researching and analyzing an applicant
who ultimately is not a desired insured
• Provides predictive modeling scores for applicants that can be used as a rating mechanism for
determining a variety of policy price/product points
• Helps identify potentially fraudulent claims
• Scores claims based on the likely size of the settlement,enabling an insurer to more efficiently
allocate resources to higher priority claims
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20. 12
Predictive Analytics
PREDICTIVE ANALYTICS’DISADVANTAGES FOR INSURERS
While nearly all insurers will find that the benefits of predictive analytics out-
weigh its costs, the techniques entail disadvantages, including the following:
• Inherent inaccuracy of the predictive model
• Cost of implementing predictive analytic techniques
• Resistance to change within the organization
• Need for clean, accurate data
A model’s potential for inaccuracy is an important consideration for insurers rely-
ing on predictive modeling. First, by definition, even a correctly specified model
cannot always be 100-percent accurate. An error term represents the portion of
the model that is unexplained. This error term can be substantial even in well-
specified models and can result in significant variation between predictions and
actual outcomes.
Errors in predictive models may also be caused by errors in model specification. A
model may include factors that are not significant predictors or factors that may
be significant predictors may be excluded, or unobserved.
The final source of error in a predictive model stems from the model’s assump-
tion that significant parameters will remain stable from its development period
through the period that it may be used. Significant changes in parameters
may invalidate a model. For example, a significant economic downturn may
substantially change the number of fraudulent claims insurers receive. If the
development period on which a model designed to predict fraudulent claim
frequency is based does not include any economic downturns, it may not properly
reflect the expected frequency of fraudulent claims during that period. Together,
these three sources of model errors may be significant.
In addition to a predictive model’s inaccuracy, an insurer’s use of predictive
analytics entails additional disadvantages, many of which are associated with
the operational changes that using predictive analysis techniques require. First,
an insurer may find that investing in the hardware and software necessary to
facilitate predictive modeling constitutes a costly investment. Poor record keep-
ing and multiple legacy systems often indicate that the insurer does not have
the clean, accurate data necessary to support a successful predictive modeling
platform, which creates the need for further investment. Finally, as with any sub-
stantial change in operations, an insurer may encounter resistance from within
to the incorporation of predictive analysis techniques that streamline operations
and reduce the demand for human resources, particularly from employees who
may feel their jobs are being marginalized.
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21. Predictive Analytics
13
Predictive AnalyticTechniques’Disadvantages for Insurers
• Inherent inaccuracy of the predictive model
• Cost of implementing predictive analytic techniques (for example,investments in new software
and hardware)
• Resistance to change within the organization,particularly from employees whose job functions are
streamlined or marginalized by the use of predictive analytic techniques
• Need for clean,accurate data
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22. 14
Predictive Analytics
FREQUENTLY ASKED QUESTIONS REGARDINGWIDESPREAD INSURER
USE OF PREDICTIVE ANALYTICTECHNIQUES
How do insurers avoid the“garbage in–garbage out”modeling conundrum?
Statistical models are only as good as the data used to develop them. Insurers
with insufficient data may need to enlist third-party data providers (for example,
rating bureaus, data vendors, and modeling organizations) to supplement their
own data to ensure that the output from the predictive models is relevant and
accurate.
What happens to the value of the relationship between the insurer and
the insured as insurance becomes less of a“people business”and more
automated?
Studies have demonstrated that the longer an insured remains with the same
insurer, the more profitable the relationship is for the insurer. However, automa-
tion of the insurance transaction reduces the transaction costs associated with
switching insurers, increasing the likelihood that an insured will shop for insur-
ance and ultimately switch providers. This could diminish the value of long-term
insured/insurer relationships. However, automation may also help the insurance
industry shed the image that who a customer knows is more important than the
risk he or she presents.
Does widespread insurer use of predictive analytics increase the rate of
commoditization of insurance products? (That is,do insurers now only
compete on price?)
Insurers have historically tried to position themselves in the marketplace based
on both quality and price. Recently however, as customers have become increas-
ingly price-sensitive, more personal lines insurance marketing campaigns have
focused solely on price. Predictive modeling may increase price competition in
the market for insurance. If that is the case, predictive modeling may actually
speed along commoditization through a reduced focus on differentiating the
quality of the coverage being offered.
Is the insurance industry’s use of predictive analytics revolutionary or
evolutionary? Hasn’t the industry always been based on trying to predict
future losses?
In the context of an insurer’s three major functions—marketing, underwrit-
ing, and claims—predictive analytics is both revolutionary and evolutionary.
Predictive analytics is evolutionary to underwriting, and revolutionary to
marketing and claims. In underwriting, predictive analytics increases efficiency
through improved technology. However, because underwriting has always
focused on predicting the future, predictive analytics in underwriting is just
another tool for performing more accurate risk analysis and price determination.
It is more revolutionary in marketing and claims. In both of these functions,
predictive analytics can greatly improve efficiency by applying technology where
little has been invested before.
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23. Predictive Analytics
15
How do insurers justify the switch from risk pooling to risk identification?
One of the core benefits of insurance is its ability to pool a group of homoge-
neous exposure units and offset the losses of a small subsection of that pool with
the non-losses of the remainder of the pool. This pooling reduces the variance
of losses within the pool and actually removes societal risk. As predictive models
become more refined and more insurers use them, the tradeoff between identify-
ing smaller and smaller subsections of the population to use as “homogenous
exposure units” versus the ability to pool and reduce risk becomes a more signifi-
cant issue. For many lines of business, such as auto and homeowners insurance,
large insurers insure enough individuals that the added refinement of the pre-
dictive model (the increase in the number of groupings) does not reduce the
advantages of pooling. Each of these groups will still be large enough to benefit
from pooling. However, smaller insurers may see a reduction in the benefits of
pooling in some of their smaller groups since they may have fewer insureds in the
group and see wider variation in the smaller groups’ performances.
What are some of the social or regulatory implications of predictive analytics?
One of the advantages of predictive modeling is that it may detect relation-
ships among the data or predictors/indicators of potential losses/claims that may
not be readily apparent to individuals or that may not be readily explainable.
However, an insurer must be able to justify charging differential premiums to
customers based on a predictive model output. For example, some consumer
organizations and regulators have resisted insurers’ use of an insurance score or
credit score as a pricing factor for policies. Insurers initially could not explain
why the relationship between credit score and loss ratios existed, thus making it
difficult to justify using the relationship to price policies. While such use of an
insurance score is becoming more widely accepted, this type of resistance may
become more likely if the predictive model factors used to justify pricing are
not intuitive. For example, what if a predictor of losses is not just the education
level a potential insured attained, but the high school he or she attended, or the
hospital where he or she was born? How insurers justify the factors a predictive
model uses may be just as important as discovering the relationships.
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