Fraudulent crime is growing more and more complex, and current fraud detection techniques are outdated and unsatisfactory.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: http://www.wnsdecisionpoint.com/our-insights/reports/detail/47/insurance-fraud-detection-and-prevention-in-the-era-of-big-data

1. wnsdecisionpoint.com
Insurance Fraud Detection with
Big Data Analytics

2. 1© Copyright 2013 WNS (Holdings) Ltd. All rights reserved1 Wnsdecisionpoint.com
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. 2© Copyright 2013 WNS (Holdings) Ltd. All rights reserved2 Wnsdecisionpoint.com
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. 3© Copyright 2013 WNS (Holdings) Ltd. All rights reserved3 Wnsdecisionpoint.com
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. 4© Copyright 2013 WNS (Holdings) Ltd. All rights reserved4 Wnsdecisionpoint.com
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. 5© Copyright 2013 WNS (Holdings) Ltd. All rights reserved5 Wnsdecisionpoint.com© 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: perspectives@wnsdecisionpoint.com
Website: wnsdecisionpoint.com