sas for claims analytics
DESCRIPTION
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 opportunitiesTRANSCRIPT
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
CLAIMS ANALYTICS
MORE INFORMATION
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
ANALYTICAL
INSURER
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
CLAIMS ANALYTICS CHALLENGES
Increasing Claims Fraud Higher premium rates
ISSUE IMPACT
Inaccurate loss reserving Lower capital returns
Unstructured data Greater manual processing
Limited resources Lower customer satisfaction
Rising legal costs Higher loss adjustment
expenses
Inefficient claims prioritization Larger loss severity
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
CLAIMS ANALYTICS PREDICTIVE ANALYTICS ACROSS THE CLAIMS LIFECYCLE
Litigation
Management
Medical
Management
Negotiation /
Disposition Evaluation Investigation Assignment
Set-Up &
Coverage Notification
Pre
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Cla
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Cla
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Seg
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Assig
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Inju
ry /
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Customer Attrition Propensity
Subrogation / Recovery Identification / Propensity to Recover
Fraud Propensity
Process Adherence / Compliance
Attorney Representation / Litigation Propensity
Workforce Productivity / Performance
Lo
ss R
eserv
ing
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
CLAIMS ANALYTICS FOUR AREAS FOR SUCCESS
Activity
Prioritization
Fraud
Analytics Litigation
Propensity
Recovery
Optimization
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
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.)
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
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 analysis
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
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 claims
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
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 you
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
WHY SAS? SAS FRAUD FRAMEWORK FOR INSURANCE
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
WHY SAS? VALUE PROPOSITION
Reduced paid claims by 7%
Prevented over $600k in fraud claims within
3 months
Improved false positive rates by 17%
Discovered high risk provider networks on
average 117 days earlier
Decreased loss adjustment expenses
attributed to lower litigation expenses
Increased recoveries by 3% to 6%
Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
MORE
INFORMATION
• Contact information:
Stuart Rose, SAS Global Insurance Marketing Director
e-mail: [email protected]
Blog: Analytic Insurer
Twitter: @stuartdrose
• White Papers:
Predictive Claims Processing
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