THE ANALYTICAL INSURER

Transcription

1 THE ANALYTICAL INSURER ATHENS MARCH 2015

2 WHAT DOES SAS HELP YOU DO? ANTICIPATE OPPORTUNITY

3 WHAT DOES SAS HELP YOU DO? ANTICIPATE OPPORTUNITY TAKE ACTION

4 WHAT DOES SAS HELP YOU DO? ANTICIPATE OPPORTUNITY TAKE ACTION DRIVE IMPACT

5 FCCI Insurance Group anticipates a 1 to 1.5 % point improvement in their combined ratio by using business analytics. For FCCI this would equate to between $4.6m and $6.9m per annum

6 SAS THE ANALYTICAL INSURER

7 THE ANALYTICAL INSURER ACTUARIAL Challenges Data management Data quality Rising underwriting expenses Increased competition Increasing legislation/regulations Inaccurate reserving Soft market and economic conditions Frequent rate revisions Telematics Catastrophe forecasting Long-tail liabilities New risk classification Lack of straight through processing Actuarial Analytics Ratemaking data platform Multi-variant pricing using advanced analytical tools Straight through processing for underwriting Real-time pricing Pay as you drive pricing Data integrity Renewal impact analysis Catastrophe evaluation Reinsurance analysis Pricing Precision Reserve Estimation Reinsurance Underwriting Analytics

8 Required a comprehensive analytics platform 16 predictive models, supporting $3.2 billion in annual premiums $14 million in new low -risk business =100% segment grow th. Avoided a potential loss of $75 million from certain executive liability accounts. Real-time view of risk exposure. and simulation Build models that help price insurance Actuarial products, Data choose Management policies to underwrite, and fast-track or investigate claims An early project netted 10 times ROI. Loss ratio improved by tw o to four points. Weeks shaved off time spent building models. Innovation in pricing and risk Full modelling environment Greater flexibility in selection of variable Includes usage of GLM, time series Easier to disseminate findings across the business Actuarial warehouse from 65 source systems and analytical solutions for actuaries Integrated w arehouse of 1.5 million customers Full risk assessment using huge data volumes to distil critical actuarial information. Pricing Precision Reserve Estimation Reinsurance Underwriting Analytics

9 VALUE STATEMENT C o p y r ig ht , SAS I ns tit ut e I nc. Al l rig h ts r es er ve d.

10 VALUE STATEMENT More granular pricing = 2 to 4 % improvement in Loss Ratio Avoid poor risks = 1 to 3% improvement in Loss Ratio Increase premium by 25 to 50% through product innovation

11 THE ANALYTICAL INSURER CLAIMS Challenges Maintain excellent service Increasing fraud Inaccurate loss reserving Rising settlement costs Spiralling litigation costs Catastrophe resource planning Ineffective salvage & subrogation processes Limited Resources Unstructured data Claims Analytics Straight through processing support Identify indicators across claims to: Reduce pay-outs Calculate propensity for litigation Prioritize claims for special attention Improve loss reserving & claims forecasting Fraud detection & prevention Detect known and discover new subrogation indicators Reduce investigation time & costs Fraud Detection Fraud Prevention Loss Forecasting Litigation Management Resource Planning Recovery Optimization

12 Detect additional fraud cases and improve investigator efficiency First 6 months identified additional 1,161 fraud cases w orth CZK 62 million Investigated 26% more cases Submitted 40% more fraud cases for criminal prosecution Increasing effectiveness of SIU., Facilitating the detection and prioritization of suspicious claims in order to help protect legitimate policyholders and claimants. Over $6.4m saved to date Plus 101 new provider cases launched w ith over $18 million More accurate detection & few er false positives Limit the economic impact of fraud linked to business growth, detect and prevent fraud, avoid expense of pursuing losses Estimate savings of 5 to 10% of all claims Competitive differentiation Low er premiums Tier 1 European Insurer Well established recoveries process Challenge was to see if SAS Analytics could further improve recovery rate Increased recovery rate from 23% to 27% Now analytics is an integral part of their claims processes Fraud Detection Fraud Prevention Loss Forecasting Litigation Management Resource Planning Recovery Optimization

13 VALUE STATEMENT More granular pricing = 2 to 4 % improvement in Loss Ratio Avoid poor risks = 1 to 3% improvement in Loss Ratio Increase premium by 25 to 50% through product innovation

14 VALUE STATEMENT More granular pricing = 2 to 4 % improvement in Loss Ratio Avoid poor risks = 1 to 3% improvement in Loss Ratio Increase premium by 25 to 50% through product innovation Recoveries increase by 3 to 6% Fraud rates reduction by 2 to 5%

15 THE ANALYTICAL INSURER CUSTOMER Challenges No single view of customer Increasing acquisition costs but New low cost channels (direct + aggregator) Lack of cross-channel integration Decreasing retention rates Ineffective segmentation and profiling Insufficient customer insight Ineffective agency performance measurement Poor conversion rates Customer Analytics Improve customer profitability Profile, segment & predict customer behaviour Manage customer experience Enhance marketing performance Multi-channel integration Recognize right channel for the right customer Distribution insight Highlight leading / lagging sales KPIs Segmentation Lifetime Value Retention Cross-sell & Up-Sell Distribution Insight Understanding Customer Experience

16 Automate marketing campaigns to drive strong lead management and proactively manage lapses Process optimization through marketing automation generates more campaigns w ith better results from the same amount of resources. Annually target the best 5,000 customer w ho are at most at risk of lapsing Focus on delivering superior service and not competing on price Discovered that 22% of customers contribute 80% of profit. Concentrating on this group saw a 25 to 50% uplift in cross sell response rates. Provide common access to marketing and sales data across the business and through to local sales offices Created client behaviour models Developed a new intranet-based marketing application for regional offices throughout France. Segment customers and provide actions to agents The portion of A and B customers could be increased by 20% in 20 months w hile the percentage of D and E customers could be reduced by 20% Segmentation Lifetime Value Retention Cross-sell & Up-Sell Distribution Insight Understanding Customer Experience

17 VALUE STATEMENT More granular pricing = 2 to 4 % improvement in Loss Ratio Avoid poor risks = 1 to 3% improvement in Loss Ratio Increase premium by 25 to 50% through product innovation Recoveries increase by 3 to 6% Fraud rates reduction by 2 to 5%

18 VALUE STATEMENT More granular pricing = 2 to 4 % improvement in Loss Ratio Lapse rates reduced by 20 to 25% Avoid poor risks = 1 to 3% improvement in Loss Ratio Marketing campaigns ROI increase by 10 to 15% Increase premium by 25 to 50% through product innovation Recoveries increase by 3 to 6% Fraud rates reduction by 2 to 5%

19 VALUE STATEMENT RISK Challenges New regulatory compliance Data availability and poor quality Unknown operational losses Incomplete view of risk Unreliable and inaccurate reporting Limited or nonsophisticated risk tools Lack of data transparency & auditability Risk Analytics Improve risk based decision strategies Complex stress & simulation testing for assets & liabilities Reduce cost of compliance Improve competitive position Optimal use of capital & reserves Create good market perception Modelling catastrophic losses Solvency II Operational Risk Market Risk Regulatory Reporting

20 Reduced costs, improved decision & established data governance processes Data quality improvement Library of business rules The criterion that guided us to select SAS w as the need to adopt a solution that w ould be capable of optimizing the governance of all data in the company, not just the data related to the issues of Solvency II Sergio Miedico Chief Information Officer, Uniqa To very briefly summarize, the system architecture is focused on a middle level, represented by the SAS Risk Management for Insurance solution w hich receives numerous information flow s from the various operating systems (P&C, life, claims, etc.) for the various group companies, performs a "cleaning," rationalization, cataloguing and mapping of the incoming flow s to guarantee the quality of data, and feeds the vertical calculation engines. The engines then calculate the risk profiles for the various areas (life, P&C, operational risk, credit risk, market risk) based on the specific internal models and resends the results to the SAS solution, w hich proceeds w ith risk aggregation and supervises reporting activities. Renzo Avesani Director, Risk Management Solvency II Operational Risk Market Risk Regulatory Reporting

21 VALUE STATEMENT More granular pricing = 2 to 4 % improvement in Loss Ratio Lapse rates reduced by 20 to 25% Avoid poor risks = 1 to 3% improvement in Loss Ratio Marketing campaigns ROI increase by 10 to 15% Increase premium by 25 to 50% through product innovation Recoveries increase by 3 to 6% Fraud rates reduction by 2 to 5%

22 INDICATIVE SAS LOCAL PROJECTS Solvency II Data Quality MIS Visual Analytics Fraud Framework Customer Analytics Capital Planning and Financial Management under NDA

23 w ww.sas.com