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Fighting Insurance Fraud with Big Data Analytics | Property & Casualty Insurance


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Fraudulent crime is growing more and more complex, and current fraud detection techniques are outdated and unsatisfactory. Insurers must rethink their existing fraud detection processes or risk losing profitability. Currently, Insurers analyze a mere 15-20% of all data available within their organization and mostly focus on that which is already in a structured form. This results in significant amounts of fraudulent claims going undetected, eroding insurers’ revenue by ~10% every year.

By analyzing data from within the insurance organization (such as log notes and adjuster’s notes) as well as from external sources (such as social media interactions of policy holders), collectively referred to as Big Data, insurers could unearth complex, unseen fraud patterns and boost their detection rate. With fewer fraudulent claims being paid out, insurers would be able to offer more competitive premiums and operate at a higher level of profitability.

In order to assess the awareness, perceived benefits and challenges of Big Data analytics, WNS DecisionPoint(TM) conducted a study of select U.S. insurers who cater to personal lines, commercial lines and combined lines of insurance. The study results provide insights into the means taken by insurers to effectively capture, store, aggregate, and analyze Big data to combat fraud at all stages of the policy lifecycle thereby:
- Reducing referral time and gaining more referrals
- Reducing investigation time and cost
- Understanding referrals better
- Reducing false-positive and false-negative rates
- Achieving a higher number of investigations per investigator

For a more thorough look at how Big Data analytics fortifies existing fraud management browse through the slideshare file or visit our site:

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Fighting Insurance Fraud with Big Data Analytics | Property & Casualty Insurance

  1. 1. Insurance Fraud Detection with Big Data Analytics
  2. 2. 1© Copyright 2013 WNS (Holdings) Ltd. All rights reserved1 6.7 6.9 60.5 62.1 28.0 27.6 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 0.0 20.0 40.0 60.0 80.0 100.0 120.0 2013 2014 Expense Ratio Loss/Claims Ratio (excluding fraudulent claims) Fraud Claims Ratio (approx.) ROE Every year, claims and underwriting fraud cost ~ $34 billion, negatively impacting insurers’ business Cost of fraud borne by US P&C insurers Fraud at point of sale  Fraud at the POS stage erodes 10% of insurance revenue  Workers' compensation fraud is higher in Southern states, where ~30% of construction workers have been wrongly classified as independent contractors, amounting to annual losses of $400 million in Florida, $467 million in North Carolina, and $1.2 billion in Texas Fraud at claims stage  Claims fraud costs insurers 5-10% of their claims volume and can be as high as 20%  Loss due to fraudulent claims in Personal line# insurance increased in last two years to reach $18.5 billion, in 2014 – Organized fraud is also high in personal line insurance. This business line reported the highest number of referrals to NICB* [10,659 questionable claims (QCs) out of 13,014 QCs] Impact Insurers’ Profitability 0% 20% 40% 60% Premiums escalation by over 5% Premiums escalation between 3-5% Premiums escalation between 1-3% *NICB - National Insurance Crime Bureau, a North American non-profit membership organization, created by the insurance industry to address insurance-related crime Sources: WNS DecisionPoint™ Survey Sources: Insurance Information Institute and National Association of Insurance Commissioners 95.2 96.6 Combined Ratio Limit Insurers’ Ability to Offer Competitive Premiums 1 # Personal line insurance include private passenger auto liability, homeowners multiple peril, and auto physical damage
  3. 3. 2© Copyright 2013 WNS (Holdings) Ltd. All rights reserved2 Need felt by insurers to shift away from traditional fraud detection methods and adopt advanced analytics techniques Traditional fraud detection methods Automated/Analytics driven techniques  Traditional methods such as internal audit, ‘Red Flag’ indicator, and scoring model, among others, primarily detect known fraud patterns using sampling techniques  Since these methods require manual intervention, there is a higher possibility of human error and longer lead times from fraud detection to the settlement of claims  Traditional approaches are also known for high false-positive rates (flagging genuine claims as fraudulent), which impact customer satisfaction 25% 50% 50% 50% 50% 75% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Lower%ofthepoliciesthatget cancelled Higherdetectionrateof suspiciouspolicy Declineinaveragetimetoidentify anissueinapolicy Lower%ofthepoliciesthat registeredclaimswithin100days ofsale Reducedpremiumleakage Loweraverageclaimsvalue Numberofrespondents 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Reducedreferraltime Morereferrals Betterunderstandingofreferrals Enhancedreporting Reductioninassessingtime Reductioninassessingcost Numberofrespondents Insurer using analytics Insurer using traditional approach or automated indicators Sources: WNS DecisionPoint™ Survey Sources: WNS DecisionPoint™ Survey Benefits of Implementing Analytics at Underwriting Stage Benefits of Implementing Analytics at Claims Stage
  4. 4. 3© Copyright 2013 WNS (Holdings) Ltd. All rights reserved3 The advent of Big Data is further changing the fraud detection and investigation dynamics  Insurers mostly analyze structured data, which represents just 15-20% of the total data that is generated by them – This data is mostly historical in nature and lose its predictive power beyond a certain point  Insurers need to adopt robust fraud management techniques that enable real-time fraud detection by efficiently and effectively processing large volumes of structured and unstructured data InternalInternal Claims Record Data Sources DataTypes CRM Billing Data Policy Information Credit History Application and claims data with NICB and ISO Log Notes Web Chat Transcripts Adjustor’s Notes Medical Report Police Records Social Media Interactions Why Big Data Analytics? Types of Big Data and Sources 0.0 5.0 10.0 15.0 20.0 25.0 Reduced referral time (in terms of # of days) More referrals - (in PPs) Reduction in investigation time (in terms of # of days) Reduction in investigation cost (in terms of %) 0 50 100 150 0 1000 2000 3000 Insurer using big data analytics Insurer using automated indicators or analytics Average cost per claim investigation (in USD) [LHS] Average SIU analyst time per claim (in days) [RHS] Source: WNS DecisionPoint™ Survey Benefits reported by insurers who have deployed Big Data analytics at the claims handling stage Performance improvement with Big Data analytics vis-à-vis improvement with automated or analytics techniques of fraud detection ExternalExternal UnstructuredUnstructuredStructuredStructured Avg. (Insurers who deployed Big data analytics) Avg. (Insurers who did not deploy Big data analytics)
  5. 5. 4© Copyright 2013 WNS (Holdings) Ltd. All rights reserved4 However, very few insurers have deployed fraud detection and prevention techniques using Big Data analytics Planning Knowledge Gathering Pilot Deployment  Set plans to guide project teams throughout the adoption phases  Determine the investments and anticipate benefits from such projects  Take into account existing and untapped in-house sources and assess the requirement of additional data types  Run pilots to determine the feasibility of such projects and its impact on organizational goals  Analyze benefits realized and run pilots again in case project fails to produce expected results  Ensure that departments exposed to Big Data analytics have understood associated governance and risk management practices  Ensure organizational requirements are in place such as integration of the siloed data; additional resources to handle higher work volumes; added capacity to store, maintain, and manage such data Adoption Phases Key Activities 21% 37% 16% 26% % of Respondents at Each Stage To know about the challenges faced by insurers while deploying Big Data analytics and key success factors for smooth adoption of Big Data analytics, read the full report. Source: WNS DecisionPoint™ Survey
  6. 6. 5© Copyright 2013 WNS (Holdings) Ltd. All rights reserved5© Copyright 2016 WNS (Holdings) Ltd. All rights reserved @WNSDecisionPt WNS DecisionPoint WNS DecisionPoint A credible insights hub for companies looking to transform their strategies and operations by aligning with todays realities and tomorrow’s disruptions. Email: Website: