Quality Control
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The document discusses quality control of insurance policy and claim data. It lists various data fields that should be consistent between a policy, such as name, gender, and occupation, and any related claims. It also provides an example where the gender in the policy and claim are inconsistent, with the claim stating a pregnancy as the cause.
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Data integrity, an integral aspect of cyber security, is identified as the consistence and accuracy that is assured of data in its life cycle, and is an imperative aspect of implementation, design, and utilization of systems which processes, stores, and retrieves data (Graham, 2017). It is estimated that almost 90 percent of the world’s data was generated in the last two year, and this goes to show the rate at which data is being availed. There are various threats associated with data integrity, for example, security, human, and transfer errors, cyber-attacks and malware just to name a few. The purpose of examination of data integrity in the context of organizations and business is due to the impact that it has on the latter’s operations and eventual success.
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Due to the lack of proper systems and structures to ensure that data integrity is at the helm of an organization’s priorities, management has found it difficult to solely rely on data and analytics to facilitate their decision-making process. What this means is that a significant number of businesses are missing out on the advantages accorded through aspects such ...
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State of Data Governance in 2021
It’s been three years since the General Data Protection Regulation shook up how organizations manage data security and privacy, ushering in a new focus on Data Governance. But what is the state of Data Governance today?
How has it evolved? What’s its role now? Building on prior research, erwin by Quest and ESG have partnered on a new study about what’s driving the practice of Data Governance, program maturity and current challenges. It also examines the connections to data operations and data protection, which is interesting given the fact that improving data security is now the No. 1 driver of Data Governance, according to this year’s survey respondents.
So please join us for this webinar to learn about the:
Other primary drivers for enterprise Data Governance programs
Most common bottlenecks to program maturity and sustainability
Advantages of aligning Data Governance with the other data disciplines
In a post-COVID world, data has the power to be even more transformative, and 84% of business and technology professionals say it represents the best opportunity to develop a competitive advantage during the next 12 to 24 months. Let’s make sure your organization has the intelligence it needs about both data and data systems to empower stakeholders in the front and back office to do what they need to do.
Importance of Data Governance
Data governance provides many benefits, such as improving the quality of data, increasing its accuracy, rendering it consistent etc.
Data Cleansing The Never Ending Quest for Lead Generation.pdf
Effective lead generation relies heavily on the quality of the data being
used. Data cleansing is a critical process that ensures accurate, reliable,
and high-quality data for lead generation strategies
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navigating-the-data-maze-understanding-data-stewardship-and-governance-2023-5...Data & Analytics Magazin
Navigating the data maze can be like trying to navigate a hedge maze blindfolded, with one hand tied behind your back. It's confusing and frustrating, but don't worry, you're not alone. Understanding data stewardship and governance can feel like learning a new language, but once you get the hang of it, you'll feel like you can conquer anything. So let's grab our compasses and machetes and get ready to hack our way through the jungle of data. Just kidding, we don't need any machetes, but we do need to understand the rules of the game. So hold on tight, because it's going to be a bumpy ride.do_dq.pdf
Data quality measures the accuracy, completeness, and consistency of data, while data observability monitors the overall health of data systems. Data observability builds on data quality by identifying, troubleshooting, and preventing data issues. Together, data quality and observability work to ensure data is useful and reliable.
What You Should & Shouldn't Tell Your Management about Data Governance
The document provides guidance on what to communicate to management about data governance. It recommends emphasizing that data governance formalizes existing informal practices, can improve areas like risk management and data quality, and does not require large investments. It advises against positioning data governance as a huge challenge, technical solution, or revolution. Business value examples show how data governance could address issues like undocumented workarounds.
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More from Kevin Pledge
CAA2018 Predictive Analytics
This document discusses strategies for improving life insurance underwriting processes. It outlines traditional multi-step underwriting stacks that can take over a month versus accelerated processes using new analytics that can provide results in minutes. The document advocates using behavioral science to improve how medical questions are asked. It also explores using new data sources like credit scores, facial analytics, and marketing data to supplement underwriting decisions. The goal is to streamline underwriting while still accurately assessing risk to provide more immediate insurance coverage at a lower cost and with a better customer experience.
Challenges and Opportunities by Steve Fretwell
The document discusses some of the key challenges and opportunities for Acceptiv Inc. in developing their online business. Some challenges mentioned include managing channel conflicts between direct-to-consumer and agent sales channels, navigating different regulatory considerations across provinces, and overcoming challenges with cross-selling different products that have separate underwriting. Opportunities include leveraging lessons learned from other successful online retailers, using customer data to provide more personalized value-added services, and standing out from competitors by optimizing the customer experience to make the online purchasing process quick and easy.
Introduction to Online Distribution Conference by Alan Ryder
The document discusses the trend of declining life insurance sales to the middle market in North America and the opportunity for online distribution to address this issue. It notes that younger buyers prefer online buying and self-service, while traditional distribution systems have become ineffective. It also outlines some of the challenges of attracting prospects, selling life insurance online, and integrating the sales and underwriting processes to allow for point-of-sale underwriting through an online system. The document proposes that online distribution can increase accessibility of life insurance in a cost-effective, scalable manner by replacing high variable costs with modest fixed costs.
Digital Branding - acquisition through to transaction by Andrew Bergstrom
Session 2 from Acceptiv Online Distribution Conference Sept 2017. Designing a front-end acquisition engine to pull prospective customers through to an online transaction platform. This presentation will identify how to build a customer acquisition engine front-end that is hyper-efficient, measurable and seamless through to an online transaction platform.
Teachers Life - an Online Case Study by Doug Baker
Session 4 from Acceptiv Online Distribution Conference Sept 2017. Three years’ ago, Teachers Life moved to an exclusively digital strategy; learn from their experience, what they would do differently and why they feel they are in a much stronger position today to capitalize from early mover advantage.
Online Insurance - Value of Partnerships
Session 8 from Acceptiv Online Distribution Conference Sept 2017.
A successful online strategy means working with partners such as reinsurers, marketing consultants, hosting, software developers, underwriters, TPAs and behavior scientists. Understand how this ecosystem works together for all-round success.
Online Insurance - Challenges and Opportunities
Session 5 from Acceptiv Online Distribution Conference Sept 2017. Breakout discussion covering challenges and opportunities in moving to an online distribution strategy
External Data in UW
Session 6 from Acceptiv Online Distribution Conference Sept 2017.
A digital sales journey requires immediate underwriting decisions made by a machine. Learn how external data can add value and how to maximize immediate decisions without devaluing the underwriting quality.
The Experience of Online Distribution Elsewhere in the World
Session 7 from Acceptiv Online Distribution Conference Sept 2017
Some say North America is at least 5 years behind the UK when it comes to online distribution. Is this true, and what lesson can we learn from the UK and other countries?
Innovation
The document discusses innovation and the importance of vision and leadership in driving innovation. It uses examples of insurance companies - Beagle Street, Teachers Life, and a large insurer - to illustrate how their approaches to functions like product development, marketing, sales, and claims management differ based on the resources available. The key lesson is that vision and leadership are what make the difference between incremental improvements and true innovation. Leaders need a vision for meaningful change and the ability to guide others towards realizing that vision.
3 Simple Lessons from Other Industries
This document outlines 3 lessons that the insurance industry can learn from other industries:
1. Go where the money is - the insurance industry should focus more on online distribution channels as that is where many customers now search for and buy insurance.
2. Disruption happens - new technologies and competitors will disrupt existing business models, as seen with online travel agents disrupting travel agents.
3. Question your paradigms - industries should challenge existing assumptions about how business needs to be done, as management consultants have done in questioning the purpose of prisons.
Predictive Modeling in Underwriting
This document discusses predictive modeling approaches for life insurance underwriting. It took a long time for predictive modeling to be applied to underwriting due to the conservative nature of life insurance and the time needed to see results. Now, more data and computing power are available. Approaches include replicating current underwriting decisions or directly modeling applicant mortality rates. Various data sources can be used, including internal, third party, and customer data. Issues in building the predictive model include how to develop and update the model over time. Companies must decide how to incorporate these approaches and start collecting relevant data.
2015 CIA Annual Meeting - Middle Market Gap
The document discusses the potential for online sales of personal life insurance policies within the next 5 years. It notes that digital disruption has changed customer expectations and behavior in other industries. Research suggests customers are open to purchasing life insurance online and find the traditional agent-based model inconvenient. The document examines approaches taken by online insurers in other markets and whether a similar model could work in North America. It proposes an online life insurance offering with streamlined quoting, application and underwriting processes to provide faster, more affordable coverage directly to customers.
Digital Disruption of Life Insurance
Presentation for the 2015 Life and Annuity Symposium in NY.
This presentation discusses the potential for life insurance to be disrupted by online distribution.
Global Innovation - Distribution lesson from overseas
There is an estimated unmet need for $1 Trillion additional life insurance in Canada. Small evolutionary changes in our market are unlikely to materially reduce this gap.
What can we learn from overseas?
ASNA 2015 - Innovation
It is clear that calling on customers one at a time is not working, as there is an estimated unmet need for $1T additional life insurance in Canada, and $20T in the US. The average age of an insurance advisor is 57 and it takes a shocking average of 47 days to issue life insurance. Kevin Pledge has disrupted the status quo in the companies that he worked for and has founded several companies providing innovative solutions. His latest venture aims to make life insurance accessible to the middle market. This session will discusses what innovation is and how insurance in North America can be transformed by selling online.
SOA Annual Meeting 2014 - Entrepreneurial Start Ups
Chris Samuel's presentation from the 2014 SOA Annual Meeting on how Beagle Street disrupted the UK market by selling life insurance online.
SOA Annual Meeting 2014 - EU Gender Neutral Pricing
This entertaining presentation by Chris Samuel on the European Union gender pricing, originally presented at the 2014 SOA Annual Meeting, provides background on the why gender neutral pricing was adopted, the industry response and raises questions about what might come next.
Demystifying business intelligence
This document discusses business intelligence (BI) in the insurance industry. It provides background on BI and describes typical reasons for implementing BI projects, such as consolidating data, making information accessible, and enhancing analytics. The document also outlines concerns with BI projects, like security and costs. It then discusses benefits of BI for insurance companies, including improved financial reporting, experience studies, and valuation extract management. The document concludes by emphasizing that BI projects require business expertise and that the goal should be moving application logic into the BI system.
Analytics for actuaries cia
Presentation given on Business Analytics for Actuaries at the Canadian Institute of Actuaries Annual Meeting June 2013
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Quality Control
- 2. Who am I?
- 3. Kevin Pledge
- 9. Quality Control - Data
- 10. <1> TPS
- 19. Policy Number Name POLICY DOB Gender Occupation etc
- 20. Policy Number POLICY Name Gender CLAIM Age Occupation Date of Claim CLAIM Claim Cause
- 21. POLICY CLAIM
- 22. Gender = Male (from POLICY) Claim Cause = Pregnancy (from CLAIM)
- 23. Data Mastery
- 24. In computing, master data management (MDM) comprises a set of processes and tools that consistently defines and manages the non- transactional data entities of an organization (which may include reference data). MDM has the objective of providing processes for collecting, aggregating, matching, consolidating, quality-assuring, persisting and distributing such data throughout an organization to ensure consistency and control in the ongoing maintenance and application use of this information.