Driving Insurance World through Science Murli D. Buluswar Chief Science Officer

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1 Driving Insurance World through Science Murli D. Buluswar Chief Science Officer

2 What is The Science Team s Mission? 2

3 What Gap Do We Aspire to Address? ü The insurance industry is data rich but ü Its ability to mine the data by ü Asking questions is antiquated therefore ü It tends to be knowledge poor 3

4 Science can make an impact across the Insurance value chain Business Functions Pricing Marketing Distribution Claims Optimal pricing and product mix Customer Contact Management Agent risk Segmentation Fraud Detection Subrogation Marketing spend allocation Broker Quality Index Claims Management Process Improvement Corporate Functions Human Resource Finance Legal Technology Risk Attrition Analysis Capital Reserve Adequacy Litigation Cost Management Big Data Initiatives Model Risk Management 4

5 Initiatives are spread across three pillars ~25% projects ~50% projects True North ~25% projects Strategic Enabling Paradigm shift Traditional Data visualizations Loss models Sophisticated approaches Price to value Customer Value Client risk analysis Transformative research Saving Lives Real options Machine learning Big data 5

6 How is Science helping Chartis? Science team is serving Chartis across the following functions: ü ü ü ü ü ü ü Understanding risk to optimize price and product mix Intermediary segmentation to impact customer acquisition Building analytics based underwriting capabilities Preventing Frauds Optimizing the Capital Reserve Acquiring and Retaining the Right Talent Applying technology to problem solving 6

7 Understanding risk to optimize price and product mix 7

8 Auto renewal elasticity models to drive profitability 6x Price Elasticity 1x 2.5x Price Elasticity measures the differentiated reactions of micro segments of customers to a series of comparable price changes Lowest Price Sensitivity Average Price Sensitivity Highest Price Sensitivity Lack of this understanding can result in lost opportunity Premium Lost Opportunity Indexed to Retention/ Premium Earned at 0% price change Optimal price change Proposed rate change Realized rate change Least price sensitive segment 15% Overall portfolio 7% This approach could contribute 75 to 200 bps in incremental margin Most price sensitive segment 5% 8

9 Auto insurance policy is priced based on driver s risk Telematics Normal risk evaluation models rely on surrogate variables to differentiate between safe drivers and risky ones Age Income But the best possible indicators Profession can only be derived from Locality actual driving behavior Gender Marital Status Accident history Educational background 9

10 Telematics help capture driver s attribute Telematics Information recording devices are fitted in the vehicles to capture: 1 How you drive 3 How much you drive 5 What you drive 2 When you drive 4 Where you drive Driving Variables Risk Models Usage Based Pricing 10

11 Bring multiple disciplines together for effective catastrophe risk management CAT Modeling Insurance exposure Physics Engineering Statistics Actuarial Science ü ü ü ü ü ü Exposure accumulation analysis Regional and construction type distribution of dollar exposures Distribution of exposures based on occupancy types ( residential, commercial or industrial) Insurance loss metrics Loss and probability of catastrophe events Portfolio expected loss Yearly Value at Risk (VAR) Probable maximum loss from portfolio Catastrophe risk model 11

12 CAT models utilize multi-module modeling framework CAT Modeling Event Generation Hazard characteristic of event Damage Calculation Event Loss Table Exceedance probability curves Exposure information Insured Loss Calculation Annual average loss (AAL) XsAAL Policy Conditions Return Period Losses Better pricing of portfolios based on AAL as well as XsAAL Probable maximum loss (PML) 12

13 Intermediary segmentation to impact customer acquisition 13

14 Which brokers bring more profitable customers? Broker Quality Index Tripartite segmentation of brokers Risk Adjusted Profit for Lines of Business } Loss Ratio } Commission } Expense Ratio } Other Factors (Solvency, NII, CoC) Broker Size } Number of clients } Total Revenue } Chartis Share of Wallet Future Potential } Cross-sell potential } Work required to secure marginal $ } Renewal rates 14

15 How do we manage customers by managing broker? Broker Quality Index Large Maintain Grow Broker clearly RAP+ Broker clearly RAP- Broker profitability unclear (e.g. b/c of expense allocation) Broker size Passive Fix Small Close Low High Potential score 15

16 Challenges in Optimal allocation of marketing spends Market Mix Modeling Ongoing marketing events ü ü ü Competitive behavior Multiple media channels Simultaneous promotions Difficult to measure Ø Ø Ø Correlation between different marketing activity & sales Which marketing vehicles offer the maximum ROI Which search words generate the highest return 16

17 Attribution modeling helping to measure the impact of each marketing action Market Mix Modeling Weekly Sales = f (Marketing Activity, Other factors) Applications: Higher ROI on marketing spends Effective management of portfolio 17

18 Building analytics based underwriting capabilities 18

19 Chartis has been shrinking its Trucking Insurance portfolio to address high loss ratio Predictive Risk Modeling Chartis is holding 2% of the accounts $250 Trucking Portfolio GWP (Mn) $200 $150 $100 $50 GWP Policy Series1 Count Policy count ( 000) 2 $ Natural Reaction: Shrinking Portfolio 0 19

20 Science Team has implemented predictive risk model to suggest an alternative approach Predictive Risk Modeling Powerful risk segmentation Expected Submission External data sources: 1. Activities Summary for vehicles 2. Recent investigations Riskiness Risky portfolio with around 74% high risk accounts Riskiness Potential benefit $13M annual underwriting profit 20

21 Smoothening the claim Process 21

22 Applying predictive analytics to improve claims processing Segmentation Real Time Event processing Non Risky Customers Least claims cost Claims is truly the Predictive moment of truth for carriers to deliver on their obligations Medium Risk and promises to their customers; based on the historically Medium low claims post-claim cost Segmentation Customers retention rate, it is also an area with lots of room for improvement Modeling Reporting and Real Time Analytics High Risk Customers Maximum claims cost 22

23 Like price elasticity, Service Elasticity can help select the optimum CE for a segment Customer centricity Indexed Incremental Attrition Initiative A Negative ROI Initiative B Positive ROI Claims Experience (Better Experience à ) ü Quantify incremental retention impact of key claims metrics by isolating claims service elasticity ü Identify optimal customer experience investment 23

24 Preventing Frauds 24

25 Deployment of statistical analytics to identify frauds in Worker Compensation Fraud Detection Percent Fraudulent Claims 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% % Percent of claims Captures 88% of the fraudulent claims in the top 20% investigations 25

26 Optimizing Capital Reserve 26

27 Science serves the organization to maintain an optimum reserve Capital Reserve Net Asset value % Net Asset value = RBC Solvency Ratio Based on standard accounting (=Book Value) Based on Risk Consideration = (Risk Based Capital) Basis for regulatory reserve Can portfolio s expected loss performance provide good view of reserve required? 27

28 by accurately assessing the expected portfolio loss Capital Reserve Frequency- Severity Framework Separately model frequency and severity of claims Portfolio level modeling to replicate real scenarios Simulation Modeling Deal with limited historical data issue Forecast expected future losses Expected Loss & Premium income based reserve calculation Release reserve 28

29 Acquiring and Retaining the Right Talent 29

30 2x more attrition among high performers reporting to lower rated managers 2x Multi-level attrition analysis giving us an insight, which otherwise could not have been addressed 30

31 High Performer s attrition costs $32M - $48M each year Accelerated Program Rewarded for performance H Future Leaders Laterals Regular Engagement and Development Programs High Performers M L Organizational Backbone Misfits Workers Let Go Functional / Managerial Mismatches H M High Potentials L Replaced with fresh talent Segmentation to treat the right employee in a right way 31

32 Applying Technology to Problem Solving 32

33 BOSE Better sound through Analytics and Research A long time back in 1956 } MIT graduate was disappointed with his new stereo set 8 years later } Founded a company that goes on to produce the best sound Do And to this day people Noise whatever Cancellation it Technology takes to make it better than it was still party to the pure before. If you do that, everything else will Noise cancellation technology was tunes come with Bose along. speakers Amar Bose introduced by Bose, primarily for air passengers Reflected sound + Direct Sound Bose tries to emulate live performance experience by relying of reflected sounds 33

34 Big Data: Game Changing Business Opportunities Ø Testing hypotheses Ø Linear Scalability Ø Different data formats Ø Low Latency data access Ø Different Modeling Techniques Ø In database analytics Real Time Customization Ø Ø Ø Data from internet purchases Social network conversations Location specific smart phone interactions Introduction Impact Integration 34

35 Techniques & Technology to harness BIG Data Non-parametric & unstructured data requires Techniques Machine learning Random Forest, Support Vector, Gradient Boosters Signals & link analysis Pattern detection Technology Hadoop, NOSQL Reduction in processing time Growth in data handling capabilities 35

36 Increase medical compliance to optimize intervention strategies and reduce claims Machine Learning Higher medical costs due to non-adherent patients Lower expenses by increasing compliance through targeted intervention strategies Machine Learning Framework Parallel Processing to mine unstructured prescription and medical history data Gradient Boosting and Neural Networks to understand complex patterns in data 36

37 Improved performance than the old framework on all counts Machine Learning 50% increased accuracy in identifying moderately adherent patients 37

38 Artificial intelligence helping Insurer to support various corporate functions } Deep Blue - First computer chessplaying system to beat a reigning world chess champion, Garry Kasparov in 1997 Kinect (Xbox 360) - Body recognition by tracking individual body parts uses extensive AI algorithms Current Applications: Future Applications: ü Image processing Medical Diagnosis ü Natural language understanding ü Customer Service ü Fraud Detection ü Consumer Behavior Analysis ü Supply-Demand Optimization 38

39 What does the Future Hold? 39

40 Future belongs to people who can turn data into products... The Big Data Scientist 1 Analytics 2 Problem Solving 3 Programming 50% to 60% shortage of talent even as far into the future as InformationWeek.com 40

41 Future holds a diverse team with IQ and EQ Economics Operations Research Behavioral economics Computer Science Financial engineering Hard sciences Mathematics Psychology Statistics 41

42 Thoughts we would like to leave you with Ø Attritional risk vs. Catastrophe risk modeling Ø Optimizing Capital Reserve across legal entities Ø How can Insurance industry harness BIG Data? Ø How can Technology help in detecting fraud? Ø How will Social Media change the way we do business? 42

43 Chartis is the marketing name for the worldwide property-casualty and general insurance operations of Chartis Inc. For additional information, please visit our website at All products are written by insurance company subsidiaries or affiliates of Chartis Inc. Coverage may not be available in all jurisdictions and is subject to actual policy language. Non-insurance products and services may be provided by independent third parties. Certain coverage may be provided by a surplus lines insurer. Surplus lines insurers do not generally participate in state guaranty funds and insureds are therefore not protected by such funds. The data contained in this presentation is for general informational purposes only. The advice of a professional insurance broker and counsel should always be obtained before purchasing any insurance product or service. The information contained herein has been compiled from sources believed to be reliable. No warranty, guarantee, or representation, either expressed or implied, is made as to the correctness or sufficiency of any representation contained herein. Chartis Inc. All rights reserved.

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