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Insurance Fraud Detection with Big Data Analytics

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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 (excludingfraudulent 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 escalationby over 5%

Premiums escalationbetween 3-5%

Premiums escalationbetween 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

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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%

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10%

20%

30%

40%

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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

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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

Dat

a T

yp

es

CRM

Billing Data

Policy Information

Credit History

Application and claims data with

NICB and ISOLog

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 referraltime (in terms of #

of days)

More referrals - (inPPs)

Reduction ininvestigation time(in terms of # of

days)

Reduction ininvestigation cost

(in terms of %)

0

50

100

150

0

1000

2000

3000

Insurer using big data analytics Insurer using automated indicatorsor 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

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Stru

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Avg. (Insurers who deployed Big data analytics)

Avg. (Insurers who did not deploy Big data analytics)

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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

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