Becoming A Predictive Enterprise: Winning In Insurance On Analytics

Transcription

1 Becoming A Predictive Enterprise: Winning In Insurance On Analytics Mike Tracy Director, Predictive Applications SPSS Inc. Nov 13, 2007

2 Agenda Company Background Predictive Enterprise Context Applications for Insurance The Winners Questions 2007 SPSS Inc. 2

3 SPSS Company Background Software company Public-listed on NASDAQ 39 year old company with heritage in analytic technologies Top 25 software company Operations in over 60 countries 12 consecutive record quarters of revenue Strong balance sheet-no debt Leadership Market leader in predictive analytics Recognized as leader by Forbes, BusinessWeek, Intelligent Enterprise, InfoWorld, CRM Magazine, and others Proven track record More than 95% of FORTUNE 1000 are SPSS customers All of the top 10 global financial services companies (insurance, banking, brokerage) 80% of the top 10 telecommunication services companies 84% of the top 25 retailers worldwide 96% of the top 25 global market research firms More than 80% of the top consumer packaged goods companies 2007 SPSS Inc. 3

4 Setting the Context Predictive Analytics helps connect data to effective action by drawing reliable conclusions about current conditions and future events A Predictive Enterprise uses predictive analytics to optimize processes and to leverage the skills and talents of its employees and partners, so it can better identify and meet the needs of valued customers and constituents SPSS Inc. 4

5 Setting the Context A Predictive Enterprise uses predictive analytics to optimize processes and to leverage the skills and talents of its employees and partners, so it can better identify and meet the needs of valued customers and constituents. The Predictive Enterprise embeds analytics into key business processes The Predictive Enterprise uses analytics to drive core business decisions Analytics Becomes a Way of Life for the Predictive Enterprise 2007 SPSS Inc. 5

6 Business Realities Drive Need for New Sources of Competitiveness The World is Flat Global parity in IT, education, manufacturing and capital Neutralizes traditional sources of competitiveness Accelerated Market Expectations Phenomenal increase in number of customer interactions Low barriers to entry easy for customers to make choices Sustainable Innovation IP grounded in customer intimacy Acquiring and retaining profitable customer relationships 2007 SPSS Inc. 6

7 Hallmarks Of The Predictive Enterprise Focusing on people data A drive toward customer intimacy Building relationships at the individual level Have direct contact with customers or want it Numerous transactions, multiple channels Addressing interconnected business objectives Maximize customer equity Acquire new customers Sell and deliver profitably Realizing profitable revenue generation as the end goal 2007 SPSS Inc. 7

8 Predictive Analytics Across Insurance Proper risk assessment and pricing Risk Evaluation Fraud Detection Claims Management Streamline claims handling Lower costs of claims $ Underwriting Customer Segmentation Reserve Accurately Financials Increase RR across any channel Core Sales Policy Management Insurance Balanced portfolio Reduced risks Increase Customer Satisfaction Complaint Analysis 2007 SPSS Inc. 8

9 Predictive Applications for Insurance Improve underwriting and pricing Straight through processing More pricing bands Improve fraud referrals Decrease false positives Know what you re missing Make referrals earlier Decrease LAE by improving processes Improve operations Improve claims assignments Provide adjuster advice and tasks Gather pertinent data from adjusters Notify supervisors early about problem claims 2007 SPSS Inc. 9

10 PredictiveClaims Process Flow Provides real-time alerts/scripts and routing decision for the claim handler Evaluate incoming claim against fraud profiles (business rules & models) claim SPSS Risk Control Server Determine appropriate action Risk assessment Routing Determination Notification/Alert Services Access relevant data for this claim Claim data Policy data Customer data External data services 2007 SPSS Inc. 10

11 Underwriting 2007 SPSS Inc. 11

12 Improve Underwriting 2007 SPSS Inc. 12

13 Improve Underwriting 2007 SPSS Inc. 13

14 Underwriting Process Intelligent scripting Instantaneously assess risk Instantaneously bind policy 2007 SPSS Inc. 14

15 Claims-Fraud Identification 2007 SPSS Inc. 15

16 The Insurance Fraud Problem Typical Fraud Referral Ratios All Claims = 1000 Defeated = 2.5 Referred = 10 Reasons for poor referral rates Adjuster workload is tedious Adjuster inexperience Conflicts with inventory control Investigated = SPSS Inc. 16

17 Predictive Analytics Against Insurance Fraud Known Fraud Historical Claims Data Undetected Fraud 2007 SPSS Inc. 17

18 SPSS Approach Business Rules Prediction Claim/Insured level Medical provider/attorney level Repair shop level Outlier Detection Claim/Insured level Medical provider/attorney level Repair shop level USES SUBJECT MATTER EXPERTISE REQUIRES HISTORICAL CASES OF FRAUD LOOK AT CASES BASED ON UNUSUAL-NESS 2007 SPSS Inc. 18

19 Leverage SPSS Experience Over 170 Known Risk Indicators Premiums are paid by cash Injuries denied initially but later changes after attorney visit or treatment begins There are changes on the policy affecting coverage or deductibles close to but prior to loss Any vehicle involved in the claim was a previously salvaged vehicle Driver is not injured and 2 or more passengers are injured Involved party is: uncooperative; hostile; unsure or vague about details 2007 SPSS Inc. 19

20 Claims-Process Optimization 2007 SPSS Inc. 20

21 Claims Operations Improvement Expedite claims processing Intelligent claim routing Improve Subrogation Adjuster performance Identify poor performers & training needs Management performance Evaluate effects of change, predict outcome Vendor performance Preferred Repair Shops Car Rental 2007 SPSS Inc. 21

22 All closures Houston closures theft closures BI closures PIP closures All closures Houston closures theft closures BI closures PIP closures Routing claims according to best fit New COMP Claim All closures Houston closures theft closures BI closures PIP closures Joe P. stats: Atty rep rate = 28.4% Average days to close = Average BI days to close = Average PD days to close = Average Comp days to close = 18.9 Best results when customers in zip codes: 786XX 78999, 78142, 782XX Andy W. stats: Average Comp days to close = 23.9 Bill T. stats: Average Comp days to close = All closures Houston closures theft closures BI closures PIP closures All closures Houston closures theft closures BI closures PIP closures Susan K. stats: Average Comp days to close = SPSS Inc. 22

23 Marketing CRM 2007 SPSS Inc. 23

24 Customer Lifecycle Management Customer Retention Understand the changes in behavior or attitudes which indicate a customer may leave, and which factors engender disloyalty Predict the retention risk of each customer Act: Make retention offers to high-risk, highvalue customers Kyobo Life reduced attrition by 13% 2007 SPSS Inc. 24

25 Customer Lifecycle Management Up-Sell/Cross Sell X X Understand the profile of customers who are best prospects for offers Predict response to an offer, value of purchase, best approach Act: Make the right offer to the right customer, in the right way, at the right time AEGON 1 in 3 conversion rate, 30M additional sales in first year 2007 SPSS Inc. 25

26 Customer Lifecycle Management Customer LTV Understand variation in current, future and lifetime value of customers Predict which customers have the potential to develop greater value, and which are at risk of decreasing in value and possibly being lost Act: Make value-based decisions on cross-sell, up-sell and retention offers, and on customer treatment Corona Direct Increased long-term customer profitability by 20% 2007 SPSS Inc. 26

27 Case Studies 2007 SPSS Inc. 27

28 ING Unlocks Claims Processing Efficiencies In Call Centers-Increased Customer Satisfaction/Detected More Fraud Business Objectives: Enable intelligent claim routing process 20% to 40% cost reduction of the claim handling process Total cost 30 million / year Increase the amount of detected fraud Doubled the amount of real detected fraud from 2M Euro to 4M Euro At a Glance Top 5 European Bank and Insurer. 15 Million Customers. 100K+ claims per year (call center 2007 SPSS Inc. 28

29 Claims Success : MetLife P & C Business objectives Create an Analytics Center of Excellence team Find new patterns of fraud, beyond the capability of the business rules engine Up-sell/Cross-sell across product lines LTV Segmentation across product lines Select the best outbound offer with the highest contribution to profit $2.2B Div. of the Metropolitan Life Company Multi direct channel Motto Financial Freedom for Everyone Financial products: Car, travel, healthcare, life insurance, consumer loans Uses direct mail, , call center and internet 2007 SPSS Inc. 29

30 Solution Implemented Replaced single regression model with risk/fraud specific models (ChoicePoint/Magnify) Use Text Mining to cull additional information from claims system Integrated results with MetLife Portal Scored results are displayed with easily understood business rules Route claim to the most appropriate Adjuster 2007 SPSS Inc. 30

31 Northeastern Regional Carrier Reduces Loss Ratios and Increases Profits Using Predictive Analytics Business Objectives: Determine client risk in advance to set premiums Significantly reduced loss ratios Increase profits Client states that using predictive analytics has given them a significant competitive advantage in identifying risk and increasing profits. At a Glance Ranked in top 60 US Property and Casualty Insurers $1.2 Billion in annual premium Regional carrier 2007 SPSS Inc. 31

32 CRM SUCCESS: FBTO Insurance Business objectives Increase the response of targeted mailings while reducing cost Leverage inbound customer contacts for marketing purposes Select the best outbound offer with the highest contribution to profit Results 35% decrease of mailing costs (40% decrease in mailings) 40% increase in conversion Up to 25% offer acceptance in call center 29% increase in campaign profit Dutch insurance company Part of the Eureko group Multi direct channel 500,000 customers, 1,200,000 policies Motto Do-it-yourself Insurance Financial products: Car, travel, healthcare, life insurance, consumer loans 450,000 customers, 1 million contracts Uses direct mail, , call center and internet 2007 SPSS Inc. 32

33 SPSS Insurance Customers 2007 SPSS Inc. 33