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Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
Big Data - An insurance business imperative
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Big Data - An insurance business imperative

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David Helmuth and Suresh Selvarangan from Deloitte Consulting LLP presented on "Big Data - An insurance business imperative" at the Insurance Data Management Association's (IDMA) annual conference on …

David Helmuth and Suresh Selvarangan from Deloitte Consulting LLP presented on "Big Data - An insurance business imperative" at the Insurance Data Management Association's (IDMA) annual conference on Apr. 8, 2014.

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  • 1. Big Data An insurance business imperative David Helmuth and Suresh Selvarangan Deloitte Consulting LLP Tuesday, April 8, 2014
  • 2. Copyright © 2014 Deloitte Development LLC. All rights reserved. 2 Agenda What is Big Data?1 Where can Big Data bring value in Insurance?2 3 The Journey to Big Data – steps to get there
  • 3. What is Big Data?
  • 4. Copyright © 2014 Deloitte Development LLC. All rights reserved. 4 Big Data is more than just growth in data volume. Big Data includes data that is unstructured, generated from non-traditional sources, and/or real-time – in addition to being large in volume. Clarifying the definition Type Size Examples Admin Kilobytes Policy Administration, Claims Administration, Billing CRM Megabytes Segmentation, Offer Details, Customer Touch Points, Support Contacts, Campaigns Web Gigabytes Web Logs, Offer History, Dynamic Pricing, Affiliate Networks, Search Marketing, Behavioral Targeting, Dynamic Funnels Big Data Terabytes Call Notes, Social Network, External Demographics, Business Data Feeds, Images, Audio, Video, Speech to Text, SMS Size of Data Big Data Web CRM Admin ComplexityofData Illustrative
  • 5. Copyright © 2014 Deloitte Development LLC. All rights reserved. 5 Creating value with the three V’s of big data Velocity Volume Variety Value + + =
  • 6. Copyright © 2014 Deloitte Development LLC. All rights reserved. 6 Identifying the types of big data in insurance Big Data is highly prevalent within insurance, but remains underutilized. Type Which V Why is it “Big” Structured Claims Data • Volume • On average, 30 years of historical claims data is stored Claims Notes and Emails • Variety • Notes and emails are considered unstructured data Telematics • Volume • Velocity • Variety • Streaming data is captured frequently (minutes); the sheer volume and velocity of the data poses challenges for traditional relationship systems Weather Patterns and Seismic Data • Volume, • Variety • Data can be provided in relational format or using geo- spatial parameters • Volume is a long-standing issue with analyzing weather patterns Social Media • Volume • Velocity • Variety • A large amount of social media data is generated • Data is transmitted in varying formats, all unstructured • Data is created at a rapid pace
  • 7. Copyright © 2014 Deloitte Development LLC. All rights reserved. 7 Enterprises face the challenge and opportunity of storing and analyzing Big Data, respectively. Insurers, in particular, may expect to be challenged with: • Handling more than 10 TB of data • Data with a changing structure or no structure at all • Very high throughput systems: for example, in globally popular websites with millions of concurrent users and thousands of queries per second • Business requirements that differ from the relational database model: for example, swapping ACID (Atomicity, Consistency, Isolation, Durability) for BASE (Basically Available, Soft State, Eventually Consistent) • Processing of machine learning queries that are inefficient or impossible to express using SQL Implications for the enterprise “Shift thinking from the old world where data was scarce to a world where business leaders demonstrate data fluency” - Forrester “Information governance focus needs to shift away from more concrete, black and white issues centered on ‘truth’, toward more fluid shades of gray centered on ‘trust.’ ” - Gartner “Enterprises can leverage the data influx to glean new insights – Big Data represents a largely untapped source of customer, product, and market intelligence” – IBM CIO Study
  • 8. Copyright © 2014 Deloitte Development LLC. All rights reserved. 8 Big Data is supported and moved forward by a number of leading vendors throughout the ecosystem. In many cases, vendors play multiple roles and are continuing to evolve their technologies to meet changing market demands. Taking a look at the big data ecosystem Big Data File and Database Management Big Data Integration Big Data Analytics Stream Processing and Analysis Appliances BI/Data Visualization Big Data Ecosystem
  • 9. Where can Big Data Bring Value in Insurance?
  • 10. Copyright © 2014 Deloitte Development LLC. All rights reserved. 10 Making big data and analytics top of mind Insurance is a tough market. Big Data driven analytics can provide an edge in both day-to- day management decisions and in finding top line growth Industry-wide investment is turning analytics from an emerging issue into a core competency: • 82% of insurance executives surveyed cite data and analytics as a key strategic priority • 81% of insurance companies surveyed intend to increase spending on data initiatives in the coming years • By 2016, it is estimated that 25% of large global companies will have adopted big data analytics for at least one security or fraud use case Manage the Business • Gain visibility into operational performance • Improve statutory and market conduct reporting • Streamline core processes • Identify fraudulent claims Doing Nothing is Not an Option • Competitors and emerging startups are changing the industry, pushing analytics from an advanced capability to a core competency Grow the Business • Create personalized pricing for customers • Build stronger distribution channels • Proactively cross- and up-sell current customers • Target opportunities in new geographies
  • 11. Copyright © 2014 Deloitte Development LLC. All rights reserved. 11 Big Data Telematics Visualizations Advanced Analytics Claims Analytics Applying big data in insurance
  • 12. Copyright © 2014 Deloitte Development LLC. All rights reserved. 12 Streaming telematics data Stream • Latitude and longitude captured at predefined intervals during a trip, typically within 1–3 minutes intervals • Average number of drivers, taking an average number of trips per day — volume grows large very quickly Event • Excess speed, acceleration, breaking, turns, and other values derived from sensors • Volume of events more variable depending driving conditions and driver behavior Trip Score • Relative score based on various factors captured during a trip EventStream Trip Score
  • 13. Copyright © 2014 Deloitte Development LLC. All rights reserved. 13 Identifying the risk
  • 14. Copyright © 2014 Deloitte Development LLC. All rights reserved. 14 Integrating telematics data to gain insights Traditional Data When combined with policy / demographic factors / claims experience / driving history, you can really start to answer the important questions. Premium leakage? Are my drivers driving more than the estimate provided during underwriting? What is the relationship between the driving behavior and driving history? Do my drivers with lower scores have higher claims? Traditional data StreamEvent Insight
  • 15. Copyright © 2014 Deloitte Development LLC. All rights reserved. 15 Moving from basic to advanced analytics Technologies around Big Data have emerged to handle exponentially growing volumes, improve velocity to support real- time analytics, and integrate a greater variety of internal and external data. Big Data and Advanced Analytics Attributes Reactive Gigabytes Weekly/monthly reporting Predefined, structured data Strategic Terabytes Weekly/monthly modeling Expanded, still structured Real-time Petabytes Real-time modeling Dynamic, includes unstructured data Decisions Volume Velocity Variety Yesterday Today Tomorrow ForesightHindsight Insight Reporting Predictive modeling Big Data and advanced analytics Hypothesis Testing
  • 16. Copyright © 2014 Deloitte Development LLC. All rights reserved. 16 Evolving the actuarial process More sophisticated customer digital interactions require and enable increasing insight into customer behavior. Organizations that leverage big data and advanced analytics can have accelerated growth through greater insight and understanding of their expanded customer interactions. New Signals Predictive models to push and alert business of opportunities and insights Profitable Growth Investments in analytics infrastructure and tools to improve insight into financial and market information Hidden Insight Social media has given rise to new ways to connect with customers and uncover patterns Computing Capacity Real-time processing and data mining are now possible Volume and Variety Global data volumes continue to grow
  • 17. Copyright © 2014 Deloitte Development LLC. All rights reserved. 17 Empowering actuaries with analytics A well-constructed and maintained Enterprise Data Warehouse frees up actuaries, analytics modelers , and data scientists to focus on the data itself and their loss/predictive models. Trying to figure out how to draw and integrate data from a number of different sources takes valuable time away from actuaries and IT. Product Analysis Design Consistent Simplified Basis Rationalized Model Inputs Methodology Analysis Policyholder Data Lapse/PUP/Surrender Rates Expenses Mortality Bond Rates Unit Allocation Rates Commission
  • 18. Copyright © 2014 Deloitte Development LLC. All rights reserved. 18 Automate unstructured claims data with analytics Analytics on unstructured data is a process to automate the interpretation of language to find the useful information hidden in documents and text within the enterprise and from external sources. Usage Unstructured analytics enables the following capabilities:  Capture early signals of customer discontent  Quickly target product deficiencies  Find fraud  Route documents to those who can best leverage them  Comply with regulations such as XBRL coding or redaction of PII Data Retrieval The Data retrieval engine searches across all relevant content to provide a summarized output Text Mining Text mining tools extract and identify relationships between entities of interest Other Capabilities  Linguistic and statistical techniques to extract concepts and patterns  Transformation of language into data  Unlocking of meaning and relationships
  • 19. Copyright © 2014 Deloitte Development LLC. All rights reserved. 19 Use analytics to improve loss outcomes Claim adjuster notes and call center notes, often stored as free-form texts, contain valuable information that can be leveraged for better claim outcomes and improve efficiency within claims organization. Big Data platforms allow insurers to perform advanced analytics on the unstructured claim adjuster notes and to provide near real-time updates, which opens up the following possibilities
  • 20. Copyright © 2014 Deloitte Development LLC. All rights reserved. 20 Realize claims efficiencies First Notice of Loss  Call center notes used to predict severity  Social media data along with notes used to predict potential fraudulent activities 1 Triage/Assign Claim  With improved severity prediction, claims are classified and assigned in timely manner, reducing costs  Improved claim segmentation leads to “best-fit” adjuster being assigned 2 Initial Claim Setup  Improved severity prediction allows more accurate reserves to be allocated 3 Perform Investigation  Adjuster can search for similar claims and replicate best practice 4 Negotiate / Settle Claim  Improved predictions lead to improved loss outcome 5 Performing advanced analytics on unstructured claims data can improve claim loss outcome and related costs by improving the efficiency and effectiveness of the claim adjuster’s claims handling activities and improving reserving practices.
  • 21. Copyright © 2014 Deloitte Development LLC. All rights reserved. 21 Leverage visualization techniques Most insurance companies have access to similar data sets; leading players use visualizations to combine these sets in complex ways to extract unique, actionable insights. Example scenario • The Chief Risk Officer or Chief Information Officer for a large Property & Casualty Carrier needs to prepare for an impending natural disaster, in this case a hurricane heading up the eastern seaboard • Information about the path of the storm, insured risk, loss prediction models all need to be evaluated in combination with team location data Examine the hurricane’s projected path using a real-time, publicly available information from NOAA Overlay the path with the book of business. Projected losses correlated to in force policies and loss projections from Catastrophic Loss Models Gain insights about where agents and adjusters are location, if they are likely to be impacted by the event, and what other field service personnel can be brought in for support 1. Examine 2. Overlay 3. Assess
  • 22. The Journey to Big Data
  • 23. Copyright © 2014 Deloitte Development LLC. All rights reserved. 23 Move from basic to advanced information management. Big Data is the next step in the evolution of analytics to answer critical and often highly complex business questions. However, that journey seldom starts with technology and requires a broad approach to realize the desired value. Expand on your capabilities Reporting Data Analysis Modeling and Predicting “Fast Data” “Big Data” Data Management Standardize business processes Focus less on what happened and more on why it happened Establish initial processes and standards Leverage information for predictive purposes Analyze streams of real-time data, identify significant events, and alert other systems Leverage large volumes of multi-structured data for advanced data mining and predictive purposes
  • 24. Copyright © 2014 Deloitte Development LLC. All rights reserved. 24 Journey to big data Develop a Strategic Plan Identify strategic priorities Identify Opportunities Brainstorm and ask “crunchy” questions Determine Data Sources Assess the landscape, current capabilities, and priorities Adopt in Production Prioritize and implement successful, high- value initiatives in production Identify and Define Use Cases Based on the assessments and business priorities, identify and prioritize big data use cases Pilot and Prototype Identify tools, technologies, and processes for use cases and implement pilots and prototypes 1 2 3 6 4 5
  • 25. Copyright © 2014 Deloitte Development LLC. All rights reserved. 25 Step 1: Develop a strategic plan Every Big Data project starts with a short planning and scoping phase. Conduct analysis Evaluate current situation Mission vision values Situation assess- ment Key issues Analysis of external sources Analysis of internal sources Synthesis Future Industry Scenarios Future industry scenarios Formulate strategy Create transformation plan BI & analytics roadmap Strategic Big Data plan Action- plans Strategic options Reward Strategic direction WorkshopsInterviews Brainstorm sessions, Workshops, and analyses Implementation plan writing Creativity and ideas Think outside of the box
  • 26. Copyright © 2014 Deloitte Development LLC. All rights reserved. 26 Step 2: Identify opportunities Identifying strategic opportunities starts with asking “crunchy” questions for “sticky” business issues. This process is independent of the underlying data (volume, variety, and velocity) and therefore applicable to both traditional and big data analytics. Sales • How many of our leads have converted into sales? • What is the profile of those leads? • What campaigns are generating the higher response rate and have the best ROI? Risk How can we eliminate offers to those adversely effected by underwriting decisions? Customers • Are our customers frequently changing products? • What are the key customer metrics across LOB’s for acquisition, retention rates, and customer satisfaction? • Who are the next 1,000 customers we’ll lose — and why? • How do factors such as politics and demographics affect the price our customers are willing to pay? Product How can we improve product pricing by analyzing data from different sources?
  • 27. Copyright © 2014 Deloitte Development LLC. All rights reserved. 27 Asking the right question can go a long way. Big Data introduces new technologies and tools for coping with the volume, velocity, and variety that characterize data sources in current business ecosystem. The opportunities are exciting, but a multitude of difficult questions first need to be answered. Step 3: Determine data sources Selection Criteria Data Structure What structure can be derived from nontraditional data sources to make storage, analysis, and ultimately decision-making easier? Governance What data governance is appropriate when analysis is distributed, needs change, and data definitions and schemas evolve over time? How is data quality managed across so many sources of data, many of which come from outside the organization, such as public social networks? Architecture What levels of availability and reliability are possible in mission-critical applications when data volumes are so large? What intellectual property, licensing, and data protection considerations apply when Big Data environments are distributed across boundaries? Infrastructure Is specialized hardware required for a particular need, or can low-cost commodity hardware be leveraged to scale processing? How can current IT skill sets best be leveraged in evolving the infrastructure to include Big Data?
  • 28. Copyright © 2014 Deloitte Development LLC. All rights reserved. 28 Step 4: Identify and define use cases Identify and define use cases to unlock the value of Big Data. Identify Identify key information needed and the data sources required. Access Access internal and external data sources to provide an integrated view of the organizational data. Analyze Analyze the data using statistical tools and techniques to discover patterns and generate insights. Act Act on the insight from the analytical models and visualizations to produce business results. Visualize Visualize the data to engage non-technical business users and focus attention on the right problems.
  • 29. Copyright © 2014 Deloitte Development LLC. All rights reserved. 29 Steps 5 and 6: Pilot and adopt Valuable time and money can be saved by adopting a business user driven prototyping approach that targets value providing initiatives. Governanceandstewardship End User Environment Collection Ingestion Discovery and cleansing Integration Analysis Delivery Production Extract & Load LOB applications FilesData marts Marketplace — external data Data quality Analysis cubes Data warehouse Transform Analysis Reports Dashboards & scorecards Analyze Business user Hypotheses / questions ? Pilot Spreadsheets, Specialized Tools, Sandboxes Value? Yes1 2 POC prototype 3 Implement AND 4 Repeat the POC / prototyping process with more value- providing initiatives Repeat process Adopt Implement successful, high- value initiatives in production Big Data environment Visualize Analytical environment Hadoop | MPP | Appliance | In-memory
  • 30. Copyright © 2014 Deloitte Development LLC. All rights reserved. 30 Contact Us If you’re ready to take your organization into the next step in the evolution of analytics to answer critical and often highly complex business questions. Start here to get the conversation started: Tami Frankenfield Director Deloitte Consulting LLP tfrankenfield@deloitte.com Suresh Selvarangan Specialist Leader Deloitte Consulting LLP sselvarangan@deloitte.com David Helmuth Specialist Leader Deloitte Consulting LLP dhelmuth@deloitte.com
  • 31. This publication contains general information only, and none of the member firms of Deloitte Touche Tohmatsu Limited, its member firms, or their related entities (collective, the “Deloitte Network”) is, by means of this publication, rendering professional advice or services. Before making any decision or taking any action that may affect your business, you should consult a qualified professional adviser. No entity in the Deloitte Network shall be responsible for any loss whatsoever sustained by any person who relies on this publication. As used in this document, "Deloitte" means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting. Copyright © 2014 Deloitte Development LLC. All rights reserved. Member of Deloitte Touche Tohmatsu

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