SAS for Claims Analytics

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Analyzing claims data at each stage in the claims life cycle – from first notice of loss to payout – is critical for making the right decisions at the right times for the right parties. SAS approaches the problem by using analytics to:
• Detect fraudulent claims
• Achieve activity optimization techniques to assign resources based on workload, experience and skill set.
• Avoid overpaying fast-track claims by optimizing limits for instant payouts.
• Mitigate the severity of a disputed claim and to assign resources most efficiently and effectively.
• Reduce loss-adjustment expenses by generating alerts regarding the probability of salvage and subrogation opportunities

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SAS for Claims Analytics

  1. 1. CLAIMS ANALYTICS MORE INFORMATIONC 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. ANALYTICAL INSURERC 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. CLAIMS ANALYTICS CHALLENGES ISSUE IMPACT Increasing Claims Fraud Higher premium rates Inaccurate loss reserving Lower capital returns Unstructured data Greater manual processing Limited resources Lower customer satisfaction Higher loss adjustment Rising legal costs expenses Inefficient claims prioritization Larger loss severityC 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. 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 / ComplianceC 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. CLAIMS ANALYTICS FOUR AREAS FOR SUCCESS Activity Recovery Prioritization Optimization Fraud Litigation Analytics PropensityC 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. CLAIMS ANALYTICS ACTIVITY PRIORITIZATION Problem • Shortage of expert adjusters and subrogation professionals have resulted in overworked and understaffed claims departments • Increased claims duration = Higher severity and lower customer satisfaction Result • Improve allocation of claims based on experience, loss type and workload • Enhance metrics / KPIs on claims professional performance • Better allocation of claims to preferred service provider (Body shop repair, property replacement, medical procedures etc.)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. CLAIMS ANALYTICS FRAUD ANALYTICS Problem • Estimated that 10% of all claims are fraudulent • Double digit growth in suspicious claims • Rise in organized fraud & criminal rings Result • Fraud analytical engine to combat opportunistic and organized fraud. • Combines a variety of analytical techniques including: • Business rules • Predictive modelling • Anomaly detection • Social network analysisC 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. CLAIMS ANALYTICS LITIGATION PROPENSITY Problem • Rising litigation costs • Claims severity is double when an attorney is involved Result • Analytics can help determine which claims are likely to result in litigation earlier within the claims process – even at FNOL • Identify litigation indicators and prioritize claim for special attention • Large & exceptional claims • Unexpected number of medical treatments • Speedier resolution significantly reducing overall costs of such claimsC 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. CLAIMS ANALYTICS RECOVERY OPTIMIZATION Problem • About 1 in 7 claims are closed with missed subrogation opportunities = $15bn in US annually • Reliance on manual process as insurers rely on adjusters to assess whether a paid claim should be recovered Result • Running predictive analytics alongside the insurers existing claims process will help reduce the number missed subrogation claims • High probability score = high likelihood of recovery • Low probability score = low chance of recovery and another insurer may look to recover from youC 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. WHY SAS? SAS FRAUD FRAMEWORK FOR INSURANCEC 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. WHY SAS? VALUE PROPOSITION Reduced paid claims by 7% Prevented over $600k Increased recoveries in fraud claims within by 3% to 6% 3 months Decreased loss adjustment expenses Improved false attributed to lower positive rates by 17% litigation expenses Discovered high risk provider networks on average 117 days earlierC 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. MORE INFORMATION • Contact information: Stuart Rose, SAS Global Insurance Marketing Director e-mail: Stuart.rose@sas.com Blog: Analytic Insurer Twitter: @stuartdrose • White Papers: Predictive Claims ProcessingC 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. THANK YOUC 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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