1. © 2014 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent. Innovative Approaches in a Tough Market Taking Underwriting to the Next Level with Predictive Analytics Hansong Choi Underwriting Strategist/ Data Specialist/Modeler Prudential of Korea Nitin Basant Analytic Science FICO

2. © 2014 Fair Isaac Corporation. Confidential. The Power of Predictive Analytics How can predictive analytics help transform underwriting in the insurance industry?

3. Agenda © 2014 Fair Isaac Corporation. Confidential. ► Introduction ► Life Insurance Market in Korea ► An Utilization of Predictive Model in Underwriting ► EUS (Expert Underwriting System) Model ► Preferred Underwriting Model ► Tele Interview Model ► New Ideas

4. © 2014 Fair Isaac Corporation. Confidential. Introduction ► Who Are We?

5. © 2014 Fair Isaac Corporation. Confidential. ► Prudential of Korea Introduction Total asset of $US 11.2 billion Net profit of $US 183.6 million Asset RBC CSI The highest level of RBC Ratio in the industry = 432.2% MDRT 92.3%, CIC 35% 13 th Persistency rate 87.9%

6. © 2014 Fair Isaac Corporation. Confidential. Life Insurance Market in Korea

7. © 2014 Fair Isaac Corporation. Confidential. Life Insurance Market in Korea The size of Korea life insurance market 91.2 billion USD, M/S 3.5% Global rank 8 th ► Enhanced Financial Supervisory Services (FSS) regulatory oversight ► Blind spot of National Health Insurance ► Intensified moral hazard Personal Burden National health insurance Medical cost

8. © 2014 Fair Isaac Corporation. Confidential. Feature of Underwriting Introduction of predictive modeling No discrimination of underwriting Limited collection of personal information The claims paid by insurance fraud is estimated $US 3.4 billion

9. © 2014 Fair Isaac Corporation. Confidential. Utilization of Predictive Models in Underwriting ► EUS (Expert Underwriting System) Model

10. © 2014 Fair Isaac Corporation. Confidential. EUS (Expert Underwriting System) ► Development Objectives EUS Model Enhance underwriter’s professionalism Improve underwriting efficiency + rule set compliment Strengthen loss ratio management Build risk DB, predictive model TargetMethod Automated Issue Ratio Risk DB and predictive model 8% N 45% Y Objective2011Introduction

11. © 2014 Fair Isaac Corporation. Confidential. EUS (Expert Underwriting System) ► Development review EUS Model BRMS (Business Rule Management System) How to handle the policies? (Operation’s aspect) Predictive Model There are subsidiary Information FICO ® Blaze Advisor FICO ® Model Builder

12. © 2014 Fair Isaac Corporation. Confidential. EUS Process and Utilization of Model EUS Model ValidatorAgencyUnderwriter Risk Mart Predictive Model DB Medical Examination Tele Interview Score Death Score Surgery Score Hospitalization Etc. Operation Work with tiny risk

13. © 2014 Fair Isaac Corporation. Confidential. Predictive Model Development EUS Model incidence within 3 years Predictive Model (scoring) risk factor1 Defining a target risk factor2 risk factor3 risk factorN Apply to new business underwriting Focus underwriting on high risk (score) cases Modeling algorithm Risk Mart DB upgrade GAM (Generalized Addictive Model) Death Surgery Hospitalization :

14. © 2014 Fair Isaac Corporation. Confidential. Definition of Hospitalization Coverage Coverage: all of hospitalization in terms of disease and accident ► Guaranteed amount: KRW 10 thousand won(10$) per a day ► 3 days contestable period, maximum 180 days ► *5,000 face amount = 5 x 10 thousand won = 50 thousand won (10$) per a day EUS Model

15. © 2014 Fair Isaac Corporation. Confidential. Utilization of EUS Model ► Morbidity ratio of accepted policies EUS Model 12345671234567 Hospitalization Score grade ► Give preferential treatment ► Automatic underwriting ► Enlarge maximum face amount ► Mitigate financial/medical underwriting High Risk Low Risk ► Inspection for all policies ► General Underwriting ► Selective tele Interview (Cumulative Morbidity ratio) 7 6 5 4 3 2 1 Total

16. © 2014 Fair Isaac Corporation. Confidential. Effect of Scoring Model Introduction ► The underwriters are starting to pay attention to not only medical information, but also non-medical information EUS Model High RiskLow Risk Medical Examination Tele Interview Financial inquiry Review All paid out claim Automated Underwriting

17. © 2014 Fair Isaac Corporation. Confidential. Benefit of Score Model ► A&H Loss ratio EUS Model

18. © 2014 Fair Isaac Corporation. Confidential. Utilization of EUS Model EUS Model Score Underwriting Accept Risk Factors 1.Agent’s hospitalization loss ratio for 1 year 2.Region 3.Occupation section 4.Insured age 5.Gender Reject Score Morbidity Ratio Loss Ratio How did you reject low score? Reject policy’s Morbidity & Loss ratio projection Underwriting Effects Projection

19. © 2014 Fair Isaac Corporation. Confidential. Utilization of Predictive Models in Underwriting ► Preferred Underwriting

20. © 2014 Fair Isaac Corporation. Confidential. Preferred in Korea Market ► Preferred Condition in Korea Preferred Underwriting Smoking Habit ► Non-smoker for the last 1 year Blood Pressure ► Systolic Pressure 110~139 BMI (Body Mass Index) ► Weight(Kg) / Height(m²) ► 20–27.9 All conditions should be met As a person can apply base plan…

21. © 2014 Fair Isaac Corporation. Confidential. Why does Preferred Insured Show Higher Loss Ratio in Every Years? ► Loss ratio Preferred Underwriting

22. © 2014 Fair Isaac Corporation. Confidential. Factor Selection ► What kind of factors affect death coverage? Preferred Underwriting Factors (= Information) Preferred

23. © 2014 Fair Isaac Corporation. Confidential. Preferred Differentiation Method ► How to choose preferred insured Preferred Underwriting ► Should meet all conditions Knockout ► Meet at least several items out of all conditions Point ► Reaching a certain score that gives a specific item’s weights and score Debit/Credit Current Korea

24. © 2014 Fair Isaac Corporation. Confidential. We Knew Lifestyle Shows a Greater Impact than Physical Condition Preferred Underwriting

25. © 2014 Fair Isaac Corporation. Confidential. How Much Weight Was Given? Preferred Underwriting

26. © 2014 Fair Isaac Corporation. Confidential. A Utilization of Predictive Models in Underwriting ► Tele Interview Model

27. © 2014 Fair Isaac Corporation. Confidential. Background ► In Korea, Selective TI (Tele Interview) Model Only specified information can be collected. Cannot be rejected as a direct cause of MIB and RX profile, MVR report. So, some information from customers should be collected in order to underwrite through Tele Interview

28. © 2014 Fair Isaac Corporation. Confidential. Selective TI (Tele Interview) Model © 2014 Fair Isaac Corporation. Confidential.28 Information Predictive Modeling Targeting Who can be target to call?

29. © 2014 Fair Isaac Corporation. Confidential. Definition of Target Selective TI (Tele Interview) Model Medical rejection + extra charge+ exclusion rider + reduced Face amount Tele Interview = 15.3% Hit ratio Target

30. © 2014 Fair Isaac Corporation. Confidential. Effective Tele Interview ► Selective TI ► Target Ratio ≤40≤50≤60>60 ≤10 mil. >10 mil. Non-medical Exam 91.8% >30 mil. Selective TI Special Exam A 4.3% >50 mil. Tele Interview >100 mil. Special Exam B 0.6% >200 mil. 4.0% >300 mil. Special Exam C >7 mil. 0.1% Special Exam D 0.1% >13 bil. Special Exam E 0.0% >15 bil. 30%↑ (Step 1) 50%↑ (Step 2) 15.3% (Now) Insured Age General Death Benefit Amount

31. © 2014 Fair Isaac Corporation. Confidential. Modeling Selective TI (Tele Interview) Model Apply preferred y/n Number of riders Sum of general death benefit amount Hit history Real premium Elapsed period Product Category Replacement contract Y/N Insured Age Apply preferred y/n Number of riders Sum of general Death Benefit amount Hit History Real Premium Elapsed period Product Category Replacement contract Y/N Insured Age N Y(Preferred) N Y

32. © 2014 Fair Isaac Corporation. Confidential. Selective TI (Tele Interview) Model ► Roc curve ► Model Result

33. © 2014 Fair Isaac Corporation. Confidential. ► Total A&H loss ratio (≤2 years) ► Target ratio Selective TI (Tele Interview) Model

34. © 2014 Fair Isaac Corporation. Confidential. New Ideas

35. © 2014 Fair Isaac Corporation. Confidential. Underwriting Model New Idea Loss ratio Medical Exam Extra Charge Inforce Marketing Preferred Simplified issue Fraud detection

36. © 2014 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent. Hansong Choi hansongchoi@prudential.co.kr Thank You!

37. © 2014 Fair Isaac Corporation. Confidential. Learn More at FICO World Related Sessions ► Harnessing Network Analytics to Better Combat Fraud ► Product Showcase: Multichannel Communication Solutions for Insurance ► FICO Roadmap for Insurance Fraud Products in Solution Center ► FICO ® Model Builder ► FICO ® Blaze Advisor ® business rules management system Experts at FICO World ► Nitin Basant ► Scott Horwitz White Papers Online ► Connecting Insurance Customers – and Decisions – with Technology Blogs ► www.fico.com/blog

38. © 2014 Fair Isaac Corporation. Confidential. Please rate this session online! Hansong Choi hansongchoi@prudential.co.kr Nitin Basant nitinbasant@fico.com

40. © 2014 Fair Isaac Corporation. Confidential. # Appendix: Selected Factors by Coverage EUS (Expert Underwriting System) Model DeathDisabilityCancer Surgery CIHospitalization 1.Agent’s total loss ratio for 1 year 2.Contractor=Beneficiary Y/N 3.Insured age 4.Occupation section 5.Disease diagnosis below 5 years 6.Region 1.Gender 2.Agent’s loss ratio for 1year 3.Change job within 2year 4.Job 5.LP loss ratio fro 1year in terms of accidental death 6.Hospitalization, surgery rider y/n 1.Age 2.LP loss ratio for 1year 3.Gender 4.Contract date 5.Cancer rider y/n 6.Relationship of policy owner and insured 7.Job 8.Notice Examination within 5 years 9.LP loss ratio for 1year 1.LP loss ratio for 1year 2.Age 3.Gender 4.Cumulative surgery Face amount 5.Number of no-warrant contract 1.Gender 2.Diagnosis History ≤ 5 years 3.Age 4.Voluntary contract 5.LP Job grade 6.BMI 1.LP Hospitalization loss ratio for 1 years 2.Region 3.Occupation section 4.Insured age 5.gender Etc. 1.Region 2.Notable LP 3.Gender 4.Relationship of policy owner and insured 5.LP Job grade