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

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

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Presented to LIAJ subcommittee at March 16, 2012

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

    • PIONEERING TECHNOLOGY, WHICH (MAY)CHANGE MANAGEMENT OF INSURANCECOMPANIESMotoharu DeiMilliman, Inc.March 16, 2012
    • Table of contents Predictive Modeling Complexity Science
    • Table of contents Predictive Modeling Complexity Science
    • 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.
    • 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.
    • 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 – ・・・・
    • Predictive ModelingExample of application In-force business Product profit Marketing Underwriting management development section (UW) section and risk section management section
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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 ····
    • 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.
    • 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
    • Table of contents Predictive Modeling Complexity Science
    • Complexity Science A technique applying a way of consideration of Complexity Science (complex type), which has long been discussed in other areas of science, such as social science, has started to be discussed recently in the insurance industry. A point that it is different from the traditional concept at first is concept of “Agent-Based Model” below. Agent-Based Model: “A concept, which explains complex phenomena by behavior of a system came into effect by a relationship of Agents having a certain characteristic and a simple behavior pattern. It is a ‘concept’ rather than a certain model or a formula.”
    • Complexity Science▪ Understanding of it by the conventional system: Input Complex relational expression Output with multiple variables▪ Understanding of it by the system of Agent-Based Model: Environment Relationship Agent 4 Output 1 Agent Agent 3 Input 1 1 Agent Output 2 Input 2 Agent 5 2
    • Complexity Science Example  Agent ⇒ Airport  Relationship ⇒ Airline route  Network of an airline route can be expressed as a graph.  “Network Science” described later discusses how airport and airline route should be increased to develop a network, which is least affected by an accident, where an airport becomes unavailable, based on the features of the graph.※ Charts are referenced from Reference material 2
    • Complexity Science ▪ Why is Agent-Based Model necessary? なぜAgent-Based Modelが必要か? – It allows “explaining” an actual complex system in more realistic way. → Once the system is understood, • It is easy to explain to the concerned people. • It allows real-time crisis management. • Important data needs to be focused on is identifiable.
    • Complexity Science Agent-Based Model · · · + Concept of “network” Example of Cellular Automata: Forest fire → “Network Science” Concept of + “free behavior of an Agent” → “Cellular Automata” + Concept of development of “environment” → “Artificial Societies” + “Involvement of a model user” → “Serious Game” Example of Artificial Societies: Sugarscrape※Charts are referenced from Reference material 2
    • Complexity ScienceHow to utilize it for insurance Case 1: “Surrender behavior simulation” (Cellular Automata) (2002, Charles Boucek and Thomas Conway)  Agent: Policyholders, insurance company (itself, competitor), agents, etc.  Relationship: Policyholder and insurer, agent contract, etc.  Assumptions: – Policyholder shall age per elapse of time. – Health condition and marital status shall also change. – Policyholder shall surrender a policy or replace a policy to other company’s per some criteria. → It allows simulations of how the distributions of policyholders would change after certain period of time assuming if the company keeps the level of premium and if it raises its premium, and how they affect the profitability.
    • Complexity Science How to utilize it for insurance (continued) Case 2: “Epidemic simulation” (Artificial Societies) (2003 – 2004, Epstein)  Agent: People who live in town  Relationship: Infection of epidemic  Environment: Home, workplace, school  Assumptions: – A person goes to workplace or school. – A person would be infected at random, if he/she stays at the same place, where an infected person stays. → It turns out that approaches such as “vaccinating all the hospital workers”, “allowing second vaccination as an option” and “isolating infected patients in hospitals” are more effective than programs actually taken in the past by reiterating simulation of situations introducing various prevention programs.※ Charts are referenced from Reference material 2
    • Complexity Science How to utilize it for insurance (continued) Case 3: “ERM: Risk mapping for risk appetite” (2011, N. Allan and others)  Agent: Risk factor (probability variable)  Relationship: Cause and effect (occurrence probability)  Assumptions: – To map correlations of risk factors using a concept of Bayesian Network and calculate occurrence probability allocated to each factor. → Complicated risks such as operational risk can be evaluated upon recognized by a “natural” system.※ Charts are referenced from Reference materials 1 & 3.
    • Complexity Science Simple question ▪素朴な疑問: – “Isn’t it just a simplified game without any reality after seeing some cases?” → Although there is such an aspect, it helps us to “understand” the types of impact on the system by changing some parameters. ▪結局のところ、私の考える複雑系のコアの利点とは core is as follows: In short, I think advantage of complex system – It allows “explaining” an actual complex system in more realistic way. (reprint) → Once the system is understood, – It is easy to explain to the concerned people. – It allows real-time crisis management. – Important data needs to be focused is identifiable.
    • Complexity Science Challenges for introduction ▪導入にあたっての課題 – Hardest point is “what does complex system methodology help is not very clear”. – How to apply a model to an actual issue? – There is no one tool for all purposes. • There are variety of tools to support development of Agent-based Model (ABM). Please see “Tools for Agent-Based Modelling”. (http://www.swarm.org/wiki/Tools_for_Agent-Based_Modelling) Please join us at discussion forum and other opportunities, if you are interested in. We are still creating real cases of applications by training interested individuals as specialists.
    • Complexity ScienceReference materials1. “Applications of complexity science to risk appetite and emerging risk” Neil Cantle, Milliman, Yorkshire Actuarial Society2. “Complexity Science – An introduction (and invitation) for actuaries” Prepared by Alan Millis, June 10, 20103. “A review of the use of complex systems applied to risk appetite and emerging risks in ERM practice” N. Allan, N. Cantle, P. Godfrey and Y. Yin, Presented to The Institute and Faculty of Actuaries, 28 November 2011 (London)