Real-time Data is Changing the Face of the Insurance Industry
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The document discusses the impact of real-time data on the insurance industry, highlighting insights from a panel of experts in 2017. It emphasizes the reliance of life insurers on external data sources for risk selection and the challenges faced in predictive modeling. Additionally, it introduces the Associate in Insurance Data Analytics (AIDATM) designation as part of an effort to educate professionals on big data analytics in the insurance field.
Real-time Data is Changing the Face of the Insurance Industry
- 1. 1 © Hortonworks Inc. 2011 – 2017. All Rights Reserved REAL-TIME DATA IS CHANGING THE FACE OF THE INSURANCE INDUSTRY Today’s Panelists: • Monique Hesseling, SynerScope • Naresh Mudunuru, Liberty Mutual Insurance Group • Denise Rogers, Travelers Insurance June 14th, 2017
- 2. 2 © Hortonworks Inc. 2011 – 2017. All Rights Reserved Insurance Priorities for 2017
- 3. 3 © Hortonworks Inc. 2011 – 2017. All Rights Reserved Top Analytics/Predictive Model Use Cases Life Insurance P&C Insurance • Source: Willis Towers Watson 2016 Predictive Modeling Benchmark Survey (U.S.) • The survey was fielded from September 7 to October 24, 2016. Respondents comprise 14% of U.S. personal lines carriers and 20% of commercial lines carriers. • Source: Willis Towers Watson 2015 North American Life Insurance CFO Survey • The survey was fielded from September 15 – October 13, 2015.
- 4. 4 © Hortonworks Inc. 2011 – 2017. All Rights Reserved Internal/External data sources Today, life insurers primarily rely on external data from medical records (72%) and prescription data (61%) Life insurers’ overarching strategic goal for using big data and predictive analytics is improving risk selection (50%) Life Insurance P&C Insurance • Source: Willis Towers Watson 2016 Predictive Modeling Benchmark Survey (U.S.) • The survey was fielded from September 7 to October 24, 2016. Respondents comprise 14% of U.S. personal lines carriers and 20% of commercial lines carriers. • Source: Willis Towers Watson 2015 North American Life Insurance CFO Survey • The survey was fielded from September 15 – October 13, 2015.
- 5. 5 © Hortonworks Inc. 2011 – 2017. All Rights Reserved Barriers & Challenges • Source: Willis Towers Watson 2016 Predictive Modeling Benchmark Survey (U.S.) • The survey was fielded from September 7 to October 24, 2016. Respondents comprise 14% of U.S. personal lines carriers and 20% of commercial lines carriers. • Source: Willis Towers Watson 2015 North American Life Insurance CFO Survey • The survey was fielded from September 15 – October 13, 2015. Life Insurance P&C Insurance
- 6. 6 © Hortonworks Inc. 2011 – 2017. All Rights Reserved Insurance Industry Big Data certification/designation AIDA™ Associate in Insurance Data Analytics (AIDA™) • The first insurance-specific education program on data analytics. The AIDA is more than just a new educational program; it’s the latest milestone in The Institutes’ century- old mission of providing essential knowledge to the property-casualty industry. • Like the CPCU and dozens of other programs and designations from The Institutes, the AIDA empowers individuals and organizations to stay current and knowledgeable on the issues affecting an ever-evolving industry. This designation combines established data principles with cutting-edge information and best practices on big data and its impact on insurers. • The AIDA provides risk managers, underwriters and claims pros a learning course to help them develop a data mindset that will become even more essential to insurance operations in the years to come. Managers, senior executives and data specialists can immerse themselves in the basics of the latest models and techniques used in modern data analytics.
- 7. 7 © Hortonworks Inc. 2011 – 2017 All Rights Reserved Appendix – Panel Questions
Editor's Notes
- #4: Two-thirds of P&C insurers surveyed currently use predictive models for underwriting and risk selection, an increase of over 10 percentage points compared to the 2015 survey. The reasons behind such an increase are clear. There is unanimous agreement from personal lines insurers about the fundamental importance of using more sophisticated predictive techniques to drive success in today’s market. Equally, many commercial lines carriers are recognizing that the traditional barrier of the relative paucity of homogenous risk data in commercial portfolios can be overcome, enabling models to contribute significantly in more unique underwriting environments. Eighty-six percent of small- to mid-market carriers rate more sophisticated risk selection as essential or very important to future success. Over half (56%) of large account or specialty lines carriers share that view.