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SAS for Insurance

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SAS for Insurance

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With more than 30 years of experience in the insurance industry, SAS can help you achieve long-term success and obtain peace of mind. Integrated and extensible insurance solutions built on a flexible business analytics framework and insurance-specific data model speed up both implementation and results, giving you a fast track to significant ROI.

With more than 30 years of experience in the insurance industry, SAS can help you achieve long-term success and obtain peace of mind. Integrated and extensible insurance solutions built on a flexible business analytics framework and insurance-specific data model speed up both implementation and results, giving you a fast track to significant ROI.

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SAS for Insurance

  1. 1. SAS FOR INSURANCE MORE INFORMATION C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  2. 2. SAS & INSURANCE • 1200+ insurance companies worldwide use SAS within these areas: • Actuarial • Underwriting • Claims • Marketing • Corporate Information • Reporting • Financial • IT • Risk C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  3. 3. SAS & INSURANCE INSURANCE SOLUTIONS  SAS Risk Management for Insurance  SAS Fraud Framework for Insurance  SAS Insurance Analytics Architecture  SAS Customer Analytics for Insurance C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  4. 4. ANALYTICAL QUESTIONS INSURANCE EXECUTIVES ARE ASKING INSURER  Who are my profitable customers & agents?  What claims can I recover?  Where are my expenses increasing?  How can I increase market share?  What are my customers saying about us?  Who is committing fraud?  Are our products competitively priced?  ….. C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  5. 5. ANALYTICAL INSURER C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  6. 6. ACTUARIAL CHALLENGES ANALYTICS  Rising underwriting expenses  Increased competition  Data integrity  Frequent rate revisions  Catastrophe forecasting  Long-tail liabilities  New risk classification  Telematics data C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  7. 7. ACTUARIAL HOW TO OPTIMIZE PRODUCT PROFITABILITY ANALYTICS  Multi-variant pricing using advanced analytical tools  GLM, Neural Networks, Loss Triangles  Straight through processing for underwriting  Real-time pricing  Data integrity  Renewal impact analysis  Catastrophe evaluation  Reinsurance analysis C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  8. 8. CUSTOMER STORY ONE BEACON (US) Business Problem • Price insurance to improve bottom line Customer Quote • Choose polices to underwrite The models that we use and build with • Select claims for investigation vs. fast resolution SAS give us a competitive Solution advantage. • SAS Enterprise Miner Todd Lehman, Vice President, Corporate Research Results • Loss ratio up by 2 to 4 points • Operational projects see 10 times ROI • Successful move into hard to price speciality lines C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  9. 9. CUSTOMER STORY FCCI (US) Business Problem • Reduce Churn Customer Quote • Compete in deregulated market SAS has speed, sophistication and power Solution • SAS Enterprise Miner Ned Wilson, Vice President Treasury & Planning Results • 1.5 percentage-point improvement in combined ratio from choosing whom to insure and from pricing products appropriately C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  10. 10. CLAIMS ANALYTICS CHALLENGES  Increasing Fraud  Inaccurate loss reserving  Rising settlement costs  Spiralling litigation costs  Catastrophe resource planning  Ineffective salvage & subrogation processes  Limited Resources  Unstructured data C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  11. 11. CLAIMS ANALYTICS PREDICTIVE ANALYTICS ACROSS THE CLAIMS LIFECYCLE Set-Up & Negotiation / Medical Litigation Notification Assignment Investigation Evaluation Coverage Disposition Management Management Predictive Claims Opportunities. Fraud Propensity Subrogation / Recovery Identification / Propensity to Recover Customer Attrition Propensity Workforce Productivity / Performance Attorney Representation / Litigation Propensity Injury / Treatment Segmentation & Loss Reserving Management Assignment Claim Process Adherence / Compliance C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  12. 12. CUSTOMER STORY CNA (US) Business Problem • Detect and prevent fraud in four separate commercial lines of business Customer Quote • Optimally direct its investigation resources on cases with higher likelihood of fraud We have an excellent partnership with SAS. They took the time to Solution meet with us and truly understand the nuances • SAS Fraud Framework for Insurance of CNA so that we could build effective predictive models for each line of our business Results • $1.6m in fraud recovery / prevention within the first 6 Tim Wolfe, SIU Director months of implementation • Detection and investigation of 15 potentially fraudulent provider networks – four times what CNA anticipated C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  13. 13. CUSTOMER STORY TIER 1 INSURER (UK) Business Problem • Well established recoveries process • Challenge was to see if analytics could improve recovery rate Solution • SAS Enterprise Miner & SAS text Miner Results • Increased recovery rate by 4% to 6% • Significant impact on Combined Ratio • Analytics is now an integral part of the claims processes C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  14. 14. CUSTOMER CHALLENGES ANALYTICS  No single view of customer  Increasing acquisition costs  Lack of cross-channel integration  Decreasing retention rates  Ineffective segmentation and profiling  Insufficient customer insight  Ineffective agency performance measurement  Poor conversion rates C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  15. 15. CUSTOMER HOW TO OPTIMIZE CUSTOMER INSIGHT ANALYTICS  Improve customer profitability  Profile, segment & predict customer behavior  Increase customer engagement  Enhance marketing performance  Multi-channel integration  Recognize right channel for the right customer  Distribution insight  Highlight leading / lagging sales productivity KPIs C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  16. 16. CUSTOMER STORY MAX NEW YORK LIFE (INDIA) Business Problem • Accurate data warehouse • Increase customer retention Customer Quote • Improve cross-sell sales In the first quarter after implementing SAS, sales to existing customers Solution jumped to more than 20 • SAS Campaign Management & SAS Enterprise Miner percent Nagaiyan Karthikeyan, Head of Business Results Intelligence and • Increase cross-sell sales opportunities by nearly 300% Analytics • 40 percent improvement in premium revenue • Reduced sales expenses through shortened sales cycle C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  17. 17. CUSTOMER STORY TOPDANMARK (DENMARK) Business Problem • Automate marketing campaigns to drive strong lead Customer Quote management instead of spending large sums of money on mass communication With SAS as a strategic partner, we ensure that • Prevent lapses in personal lines we have the best technology and knowledge available. The Solution vision of the data mining • SAS Marketing Automation project is to find the relevant customers far more elegantly, and ensure that they stay Results with us • Generate more campaigns with improved results from the same amount of resources Bjørn Verwohlt, Marketing Director • Annually target the ‘best’ 5,000 customers with highest risk of lapsing C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  18. 18. RISK ANALYTICS CHALLENGES  New regulatory compliance  Data availability and poor quality  Unknown operational losses  Incomplete view of risk  Unreliable and inaccurate reporting  Limited or non-sophisticated risk tools  Lack of data transparency & auditability C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  19. 19. RISK ANALYTICS BEYOND RISK COMPLIANCE WITH SAS C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  20. 20. CUSTOMER STORY CHARTIS (US) Business Problem • Estimate risk of future losses • Help underwriters access and price insurance risk Customer Quote • Estimate bad debt reserve funds for premium receivables We are now much more confident in making Solution reinsurance decisions. Today we have a daily, • SAS Analytics real-time view of our risk John Savage, Vice Results President, Strategic Risk Analysis • $14m in new, low-risk business, representing 100% segment growth • Avoided potential loss of $75m from certain executive liability accounts • Reduced requirement for bad-debt reserve funds C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  21. 21. CUSTOMER STORY HDI ASSICURAZIONI (ITALY) Business Problem • Meet Solvency II requirements while improving data quality and decision-making speed Customer Quote We have met the double objective of improving Solution data quality and streamlining information • SAS Risk Management for Insurance processes Francesco Massari, Head Results of Organization and Information Systems • Improve data quality • Timely information reaches business users, actuarial scientists and senior management C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  22. 22. SAS FOR VALUE PROPOSITION INSURANCE More granular pricing = 2 to 4 % improvement in Combined Ratio Avoid poor risks = 1 Capital allocation to 3% improvement decrease by 1% in Loss Ratio Reinsurance Lapse rates reduced Analysis = 0.2 to by 20 to 25% 0.5% improvement in U/W Expenses 3 to 5 times Fraud rates increase in reduction by 2 to 5% response rates Marketing campaigns ROI Recoveries increase increase by 10 to by 3 to 6% 15% C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  23. 23. MORE INFORMATION • Contact information: Stuart Rose, SAS Global Insurance Marketing Director e-mail: Stuart.rose@sas.com Blog: Analytic Insurer Twitter: @stuartdrose • White Papers: Analytical P&C Insurer Analytical Life Insurer C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d .
  24. 24. THANK YOU C op yr i g h t © 2 0 1 2 , S A S I n s t i t u t e I n c . A l l r i g h t s r es er v e d . www.SAS.com

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