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# Pioneering Technology, Which (May) Change Management of Insurance Companies

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Presented to LIAJ subcommittee at March 16, 2012
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### Pioneering Technology, Which (May) Change Management of Insurance Companies

1. 1. PIONEERING TECHNOLOGY, WHICH (MAY)CHANGE MANAGEMENT OF INSURANCECOMPANIESMotoharu DeiMilliman, Inc.March 16, 2012
4. 4. Predictive Modeling ▪Predictive Predictiveとは・・・ What is Modeling Modeling? → To sum up, it is “a technique to statistically project the future using technique of data mining” . What is “data mining”? ▪では「データマインニング」って何? → It is to get heuristic understanding of correlations and consequences in the data that have not been recognized by exhaustively analyzing large volume of data.
5. 5. Predictive Modeling “Data mining” has achieved the following success for example.「データマインニング」は例えばこんな成果を挙げてきました。 An economist and a wine lover, Orley Ashenfelter, derived the following theoretical formula, which calculates quality of wine, by data interpretation. Quality of wine ＝ 12.145＋0.00117×rainfall during winter＋0.0614 ×average temperature during growing period － 0.00386×rainfall during harvest period → Although it was ridiculed by wine critics who are “specialists in the field” at first, it turned out to be able to project the quality of wine more accurately. Amazon’s “recommendation” function, Web advertisements → Data mining determines “a person who bought A and B should be interested in Ｃ”, where the data of products bought and pages clicked in the past of a page viewer has been accumulated.
6. 6. Predictive Modeling To identify relationship of the data that had not been recognized by analyzing large volume of data and utilize it for a certain purpose for the field of insurance, too. Many companies, particularly in US, have introduced data mining tools for P&C area. Application of data mining for various life insurance purposes is expected, too. – Policies with what kinds of characteristics tend to be surrendered – Potential customers with what kinds of characteristics tend to purchase what kinds of insurance – To identify risk of selling limited declaration type and/or no screening type – To differentiate products – To achieve lower premium for preferred risk – For cost down and speed up of screening – To accurately project trend of mortality and/or morbidity rates – ・・・・
7. 7. Predictive ModelingExample of application In-force business Product profit Marketing Underwriting management development section (UW) section and risk section management section
8. 8. Predictive ModelingExample of application for marketing section Product In-force business Marketing Underwriting profit management development section (UW) section and risk management section section Traditionally, – Acquisition of policies by expanding potential customer base through connection of sales reps and/or consulting sales and other communications by the staff and planners – Acquisition of policies with less strict screening through DM and/or website, marketing to particular corporations, bank-counter sales – …. Predictive Modeling allows cost reduction through automatic selection of distribution channels of new products and/or automatic segmentation of DM recipients, and competitive superiority by offering business for segments that had not existed. It can also be utilized for effective training of sales representatives.
9. 9. Predictive ModelingExample of application for UW section In-force business Product profit management Marketing Underwriting development and risk management section (UW) section section section Ordinary UW includes declaration of current health condition, history of sickness and occupation, interview of a reviewer or a doctor, submission of health examination report, and sometimes blood tests in case of high-end products, that requires cost. As it also needs some time especially for high-end products, potential customer may loose his/her appetite to buy or move to other firms during the waiting period. Comparison of mortality rates Predictive Modeling can Predictive Model vs. Full Underwriting Image of UW cost reduction automatically identify a group of people with high risks. It is expected to be used to simplify UW, or specify a group that needs whole process of UW.※Charts are referenced from Reference material 2.
10. 10. Predictive ModelingExample of application for product development section Product In-force business Marketing Underwriting profit management development section (UW) section and risk management section section Traditionally, products have been developed by offering lower premium for younger age groups and/or segmenting premium for smokers. Predictive Modeling is expected to be utilized for product development which offers competitively priced products and those for cross-sales with segments that are different from traditional classifications such as health condition and/or smoking status.
11. 11. Predictive ModelingExample of application for in-force business profit management and risk managementsection Product In-force business Marketing Underwriting profit management development section (UW) section and risk management section section  In-force business risks shall change (decline of selection effect, anti- selection, move of standard risk to preferred risk, etc.)  Predictive Modeling is expected to be utilized to understand what risks are held in certain groups of in-force business, what groups tend to be surrendered, and to what groups a retention program should be focused.
12. 12. Predictive ModelingRepresentative methodologies of Predictive Modeling  GLMStatistical methods  Bayesian statistics  Generic AlgorithmNon-statistical methods  Neural Network （Machine Learning）  Decision Tree  ScoringRecent study reveals that use of a customized methodology suitable for apurpose shows better results than those using a certain methodology only.
13. 13. Predictive ModelingImage of Decision Tree To mechanically develop a tree Using a tree suitable for a purpose Single Yes No Income of over Segment 2 X million yen Yes No Segment 3 Purchase at banks Yes No Segment 4 ····
14. 14. Predictive Modeling Challenges for introduction Company data External data – Model development techniques – Administration structure • ＩＴ • Reform of corporate culture • Training of internal staff having literacy Predictive Modeling – Data to be used Algorithm To start using supplemental purposes rather than replacimg the existing methodology at once To receive a tool and support from a vender On-line UW system, sales reps., accounting department, To use free tools (such as Weka and R) etc. To gradually accumulate the company data, while using the external data as a source data at first, as the company Existing methodology data is ideal.
15. 15. PREDICTIVE MODELINGREFERENCE MATERIALS1. “Super Crunchers” by Ian Ayres, translation by Hiroo Yamagata published by Bungeisyunjun2. “Predictive Modeling for Life Insurance”, Deloitte Consulting LLP, April 20103. “Predictive Modelling for Commercial Insurance”, General Insurance Pricing Seminar, 13 June 2008 London4. Free design material site “来夢来人” http://www.civillink.net/fsozai/illust4.html