Smarter Analytics Leadership Summit Content Review

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1 Smarter Analytics Leadership Summit Content Review

2 Agenda Fraud Point of View IBM Claims Fraud Solution Overview Infinity Insurance: Combating Fraud with IBM Claims Fraud Solution Building the Business Case 2

3 Cross Industry Problem focused Industries include Anti Money Laundering Sanctions screening KYC scoring Suspicious Activity Online Fraud Transactional Credit Card Internal/Employee Loan or Application First Party Fraud Banking Insurance Claims Fraud Cash for Crash Staged accidents / thefts Exaggerated claims Provider fraud Policy fraud Fake policies / documents Rate jumping Online fraud Internal / Employee fraud Fake policies / documents Pocketing premiums Workers Compensation Tax/Revenue IRS, SSA Healthcare Medicaid Medicare DoD others Food & Nutrition Programs Food Stamps WIC Compliance and Audit Departments Agencies Social Services Public Sector Health Care Provider/Vendor Fraud Pharmaceutical Fraud Physician / Employee Fraud 3

4 We integrate technology, people, and experience Consulting Expertise Many years of experience and over a hundred global fraud, waste and abuse implementations Intellectual Capital (Asset) Thousands of cross industry risk indicators Best-in-breed software capabilities enable organizations to harness the IP and Expertise in a complete Fraud, Waste and Abuse solution Software Assets and Capabilities 4

5 What our insurance clients telling us about fraud Fraud is pervasive, growing and has become a board room issue Fraudsters are much more sophisticated, technology enabled and transnational Fraudulent activity continues to rise, methods more sophisticated Organized crime rings are more prevalent Continued and growing concern about e-commerce attacks Insiders (employees) are a prominent threat Enormous amounts of data to uncover fraudulent activity is a daunting task (Structured & Unstructured) There is simply not enough resources to attack the problems 5

6 IBM s Claim Fraud Point of View For Smart Insurers, claim fraud is not treated as a point solution or as a step in the process. It is more than a score. It starts before Intake. It is seen as preventable, predictable, provable, and is managed pervasively across the claim life cycle 6

7 Attacking fraud is one aspect of transforming to Smart Claims Smart Claims initiatives use Business Analytics & Optimization go beyond automation and core transformation to optimize outcomes across all aspects of the claim life cycle Business Analytics & Optimization to Improve Claim Outcomes 7

8 One potential source for improving Combined Ratios is reducing Claim Fraud Claim Fraud may account for as much as 10% of a carriers Incurred loss expense which is especially challenging in today s market with Combined Ratios rising well above % Fraud is a hidden cost not fully known Historically accepted as a cost of doing business Very expensive to find and reduce as was very labor intensive Market conditions are pressuring the bottom line Industry estimates suggest 10% of claims are fraudulent, but the Soft market pressuring revenue and profit actual figures are likely higher. Low interest rate limiting investment returns Historic best practice only find 1-3% Operational costs already cut to the bone of fraudulent claims Technology is poised to change Crime rings are increasingly turning to fraud this and allow the economic recovery Organized crime is seeing a low risk, high reward potential of more fraud Banking is more difficult due to AML, so seeing a shift to Insurance Lower risk courts are clogged with violent crimes Fraud Detection Economic downturn historically leads to greater fraud and abuse Opportunistic fraud rises during bad economic eras $15+ Billion Advances in analytics make finding fraud possible & economical savings Higher volume and velocity of data available to identify fraud opportunity Cheaper, more sophisticated, and faster running models 1 to 3% Carriers are increasing their investments in finding and preventing fraud 0% All Claims 10% Fraud 8

9 Carriers are expanding their focus to detect and prevent more types of fraud Fraud originates from various sources using creative schemes. The combination of new technology and challenging business conditions makes uncovering Opportunistic fraud economically viable Common focus for most P&C (and some Life) carriers. Room for improvement as this is being fought with traditional tools Mature for high volume or regulated Not as strong for low volume/non- Regulated providers Organized Opportunistic Carriers find the obvious through normal adjudication process. Room for improvement to find less obvious using better tools. Currently viewed as too costly to uncover anything but the obvious. Viewed as a Cost of Doing Business 9

10 Carriers can be much smarter when it comes to finding and stopping fraud Today s insurance companies must transform to reduce costs Lack of Insight Traditional Approach Sense and respond (After the Act) Instinct and intuition (Adjuster Driven) Smart Claims Approach Predict and Act (Predict at Intake) Real-time, Fact-driven (Data Driven) Volume Inability to Predict Inefficient Access Variety Velocity Skilled Adjusters (Instinct) Back office (SIU) Automated (Claims Admin) Everyone (Insight) Point of Impact (Agents, Adjusters, Investigators) Optimized (Case Management) Results in rapid, informed and confident actions 10

11 IBM Claim Fraud Appliance IBM Corporation

12 Robust Approach To Fraud Prevention, Detection and Investigation Identity Resolution / Social Network Analysis Resolves identities across claims Identifies relationships across entities Uniquely identifies claim entities and relationships Business Rules Industry specific rules Ex: More patients per day than is possible Business expertise rules Rules Library Predictive Models and Anomaly Detection Combines structured and unstructured big data Identifies anomalous behavior claim, provider, employee, etc. Predict Suspicious and Anomalous Behavior Case Management Centralized referral management Collaboration for improved efficiencies and results Integrated text mining of social media and other big data forms Manage, Automate, and Collaborate Cases Fraud Insights Monitor fraud metrics ensures performance Provides insights to front-line managers and executives Closed Loop Analysis for Fraud Metrics 12

13 Identity Resolution Who is who? Interactions Observations Entity Context OCC Gang Arrest Record #70001 Marc R Smith 123 Main St (713) DL: Record #9103 Randal M Smith DOB: 06/17/1934 (713) Record #6251 Mark Randy Smith 456 First Street (713) DL: EAS Entity #9451 Attribute Value Source Name Marc R Smith A Address 123 Main St A Phone (713) 730 EAS 5769 Entity #9453A Tax Attribute ID Value EAS Entity #9453A Source License A Name Attribute Marc Value R Smith A Source Name Randal M Smith B-9103 Name Marc R Name Mark Randy EAS Smith Entity Smith #9452 A C-6251 Name Mark Randy Smith C-6251 Address 123 Main St A Address Attribute 123 Value Main St A Source Address 456 First St C-6251 Address 456 First St C-6251 Phone Name (713) Randal 730 M 5769 Smith A B-9103 Phone DOB (713) A Phone 06/17/1934 (713) B-9103 Phone (713) C-6251 Phone (713) C-6251 B-9103 Tax ID A Tax ID A License A License A License C-6251 License C-6251 DOB 06/17/1934 B

14 Social Network/Link Analysis Visual and mathematical analyses of complex human and computer networks Discover sources and distribution of power: Who knows whom; who does business with whom; and who wields greatest power See how communications flow in networks and sub-networks Monitor patterns between communications nodes, performance and key goals Combines data extraction, manipulation, analysis and visualization 14

15 Decision Management Rules Support Rules Library and Execution Incorporates domain and business expertise to identify suspicious claims Configure business rules easily No programming No IT required Rule to value based assignment Different rules have different risk Create and manage thresholds Graphical, thin client interface Simulate and test scenarios 15

16 Predictive Analytics and Anomaly Detection Uncover Fraud Patterns Combines structured claims, policy and billing data with unstructured data like claim notes Identifies behavior that is uncharacteristic of similar claims, providers, attorneys, etc 16

17 Adjuster and Investigator notes can be used in fraud detection models Combining this vital data with customer, policy, and claim data provides the most robust application for fraud detection 17

18 Detection Engine: Optimize Fraud Decisions for Better Results Flexible and adaptive environment for fraud identification Combines Rules, Predictive Analytics, and Social Network Analysis Real-time claims monitoring Business led application Easily modify rules and decision outcomes Little reliance upon IT Less reliance upon Quant department Simulation and What/If Analysis Sophisticated visualization for identifying non-obvious patterns and relationships 18

19 Case Management Advanced Case Management Case Application Design Case Templates Case Tasks Case Analytics Case Runtime Framework Case Infrastructure 360 o View of Case Case Context Case Lifecycle Management Content Events Workflow Rules Collaboration Social Software Monitoring & Analytics people o process o information Leverages Web 2.0 standard with user configurable widgets, Event processing, built in text analytics, instant messaging and collaboration tools 19

20 Claims Management Dashboards Provide Immediate Insights 20

21 The IBM Claim Fraud Appliance was invoked in real time and it was determined to be a high risk The Fraud Rules presented a low probability of fraud However, the analytical model using claim description notes indicated a high probability of fraud 21

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