Insurance Fraud Detection:
Reducing Loss Payout
Using Predictive Modeling
Mark Rusch
Vice President – Sales
mrusch@statsoft.com
708-428-4113
data analysis

data mining

quality control

web-based analytics
U.S. Headquarters: StatSoft, Inc.  2300 E. 14th St.  Tulsa, OK 74104  USA  (918) 749-1119  Fax: (918) 749-2217  info@statsoft.com  www.statsoft.com
Australia: StatSoft Pacific Pty Ltd.
Brazil: StatSoft South America
Bulgaria: StatSoft Bulgaria Ltd.
Czech Rep.: StatSoft Czech Rep. s.r.o.
China: StatSoft China
France:StatSoft France
Germany: StatSoft GmbH
Hungary: StatSoft Hungary Ltd.
India: StatSoft India Pvt. Ltd.
Israel: StatSoft Israel Ltd.
Italy: StatSoft Italia srl
Japan: StatSoft Japan Inc.
Korea: StatSoft Korea
Netherlands: StatSoft Benelux BV
Norway: StatSoft Norway AS
© Copyright StatSoft, Inc., 1984-2010. StatSoft, StatSoft logo, and STATISTICA are trademarks of StatSoft, Inc.
Poland: StatSoft Polska Sp. z o.o.
Portugal: StatSoft Ibérica Lda
Russia: StatSoft Russia
Spain: StatSoft Ibérica Lda
S. Africa: StatSoft S. Africa (Pty) Ltd.
Sweden: StatSoft Scandinavia AB
Taiwan: StatSoft Taiwan
UK: StatSoft Ltd.
Overview
■ Introductions
■ Customer introductions
■ StatSoft introductions
■ A brief overview of analytic approaches, methods, and issues in fraud
detection
■ To review methods useful for detecting underwriter, provider, and
claimant fraud
■ Review Customer benefits example
■ Wrap up and Next Steps
© Copyright StatSoft, Inc., 1984-2010. StatSoft, StatSoft logo, and STATISTICA are trademarks of StatSoft, Inc.
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Through early identification of high potential fraud claims,
Predictive Fraud Detection can make a significant impact on
overall loss cost.
One LOB Claims Predictive Model Savings
Annual Premium
Loss Ratio
$136M
52%
Annual Losses Paid (est)
% loss due to fraud claims
$71M
5%
Annual losses due to fraud claims $3.5M
Fraud Claims Identified by PM
90%
Fraud Losses Identified by Model $3.2M
Projected Reduction on losses
4.5%
Annual Fraud Reduction
Revised Projected Loss Ratio
~$2M
49.8%
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StatSoft WorldWide Offices & Sample
Insurance Customers
© Copyright StatSoft, Inc., 1984-2010. StatSoft, StatSoft logo, and STATISTICA are trademarks of StatSoft, Inc.
English
Italian
French
Chinese
German
Russian
Polish
Korean
Czech
Portuguese
Spanish
Japanese
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Why Predictive Modeling
■ To Reduce Combined Ratios through:
■ Increased Subrogation and Recovery dollars by identifying
candidate claims early, tagging and tracking them
■ Uncovering usual and new types of fraud via Text mining of claim
notes, PDFs, adjustor reports, for suspicious patterns
■ More efficiently handle claims by providing “right level of service”
■ Through targeted “In Person Contact”
■ Reducing loss frequency
■ By leveraging data mining to uncover previous undetected
patterns and applying these patterns into the Underwriting
Process to either:
■ Reject Risk
■ Charge more Premium
■ Right Tracking
■ Identify claim complexity early, then route to appropriate
resource
© Copyright StatSoft, Inc., 1984-2010. StatSoft, StatSoft logo, and STATISTICA are trademarks of StatSoft, Inc.
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Predictive Analytics Enhances Many
Insurance Processes
Underwriting
Marketing
Automated underwriting / risk selection
Campaign optimization
Straight-through rate processing
Customer segmentation
Active risk portfolio management
1:1 marketing
Automated discount/credit recommendation
New product market analysis
Automated renewal processing
Outbound Predictive Marketing
Underwriting fraud detection
Inbound intelligent cross sell
Appetite selection management
Optimize leads delivered to Agency Force
Automated premium audit
Claims
Sales & Service
Fraud detection
Field force optimization (marketing & agency)
Fast tracking of claims
Commission modeling and optimization
Claims assignment automation (by competency)
Cross-sell, up-sell, offer optimization
Settlement analysis
Intelligent call routing
Accelerated detection of severe claims
Intelligent Recommendations
Predict Complexity
In and outbound Customer Retention offers
Routing optimization
Enterprise Feedback Optimization
Predict Reserves
Agent /Broker Performance effectiveness
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Opportunities
PREDICTIVE MODELING IN
ACTION
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Current Insurance Environment
■
■
Increasing deregulation and growing competition in the
insurance industry is placing pressure on insurance companies
to be more customer‐centric for the “right” customers in their
operations
In particular, focusing on providing the “right” level of service to
the “right” customers
■ For Claims – Providing real time scoring across the entire
claim process to continuously monitor for
■
■
■
■
■
■
■
Reserve Changes
Subrogation Opportunities
Fraud
Right Tracking
In Person Contact (IPC)
Claim Complexity
For Marketing – Identifying and providing the best price to
the customer with the highest lifetime customer value
■ Smart Reasons to contact beyond the renewal
■
■
Cross Selling
Targeted Retention Offers based on Customer Value
Score
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Predictive Analytics can drive down claim
costs at many points across the claim
lifecycle
Injury/
Accident
First
Report
Assign
Claim
3 Point
Contact
Low
Touch
Supervisor
Review &
Assignment
New
Information/
Medical
Pharmacy Bill
Manage
Claim/Make
Payments
Referral
Escalation
Close
Nurse Case
Management
© Copyright StatSoft, Inc., 1984-2010. StatSoft, StatSoft logo, and STATISTICA are trademarks of StatSoft, Inc.
Evaluation –
Strategy and
Reserves
Model
Score
+
Reason
Codes
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Leveraging Predictions within Claims
Workflow
■ Reducing fraud payouts by
catching fraud earlier,
■ Increasing subrogation recovery
by identifying subro cases earlier,
tagging and tracking them
■ By streamlining the processing of
non-fraudulent and routine claims
■ By identifying and sending the
most complex claims to the right
adjustor
■ By reducing working capital
requirements through more timely
and accurate reserving
© Copyright StatSoft, Inc., 1984-2010. StatSoft, StatSoft logo, and STATISTICA are trademarks of StatSoft, Inc.
Most systems can score a claim
on a batch basis, few if any can
perform this scoring in real time
against the latest claim data
entered.
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Leveraging Predictions within SIU
Workflow
■ Look at your current capacity to
handle new cases
■ Enter the capacity field
■ By streamlining the processing of
non-fraudulent and routine claims
■ System will re-score all new and
existing claims (existing claims
have new text and other
information) and create a new list
of cases that have the highest
Fraud Score based on your
current capacity levels
■ Scoring of claims for fraud is no
longer a “one time” but rather
continuous and virtually automatic
event
© Copyright StatSoft, Inc., 1984-2010. StatSoft, StatSoft logo, and STATISTICA are trademarks of StatSoft, Inc.
Most systems can score a claim
on a batch basis, few if any can
perform this scoring in real time
against the latest claim data
entered.
10
Opportunities
FRAUD DETECTION
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Fraud: Finding the Needle in a Haystack..
Without Knowing what a Needle Looks Like
■ There are many ways in which insurance fraud can be perpetrated
■ By not being “honest” on the application for insurance (underwriter fraud)
■ By not being “honest” about a specific claim (claimant fraud)
■ By systematically “manufacturing” a claim (e.g., personal injury-related
reimbursements to a provider (“provider fraud”))
■ A fundamental problem is that, unlike fraud in other domains, fraudulent activity
may go undetected for a long time, or may never be detected
■ So the problem is one of “looking for a needle in the haystack”, but not being
sure what a needle looks like
■ Not easy!
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Challenges in Identifying Fraud
■ Current Challenges
■ More fraudulent claims were slipping through the cracks.
■ Backlogged adjusters are so wrapped up in getting the claim settled that
they miss basic fraud issues
■ Manual fraud detection approaches cause delays in getting files to the SIU
department.
■ Resulting in:
■ Fraudsters realizing the above and are becoming much more sophisticated
than the Insurance Company
■ Losses paid due to fraud are on the increase
■ New types of fraud are occurring with greater frequency
■ Fraud tends to increase with a down economy
■ Fraud is now being perpetrated via social networks
There must be a better way….
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Categories of Approaches
■ Supervised Learning: Where an outcome variable exists in historical data
■
Based on the analysis of claims previously investigated by SIU
■ Predictive models can make the SIU process more efficient and effective by
identifying new types of fraud patterns through:
■ The use of new algorithms
■ New insights gained through mining of unstructured data such as adjustor notes,
faxes, PDF’s, letters and other forms of text based data
■ Leveraging Third Party Data Sources
■ Unsupervised Learning: Where an outcome variable does not exist in
historical data
■
Based on the analysis of all claims filed in the past, unsupervised models can be built to
identify claims that are “unusual” or “too usual” (too average)
■ Unsupervised learning methods may improve the SIU referral process, by identifying
more claims and new types of fraud
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What: Your Data and Past Claim
Experience is Leveraged
■ Your data surrounding past Fraud cases
■ Loss Date against policy Effective Date
■ Time and location of loss against type of injury
■ Text data (letters, faxes, claim notes, police reports, etc)
■ Any Third Party Data available or you leverage today…
■ NICB
■ State Insurance Department
■ CLUE, ISO
■ Adjustor experience
■ Interview “what do you typically look for or what makes a claim look
suspicious to you?”
■ Location, circumstances, timing, injury types, ……..
© Copyright StatSoft, Inc., 1984-2010. StatSoft, StatSoft logo, and STATISTICA are trademarks of StatSoft, Inc.
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How: Predictor Variables are Generated
■ The claims process extends over time; the earlier
fraud activity can be discovered the better (the greater
the savings)
■ For example, a flag capturing that treatment for
back pain by a chiropractor is ongoing 2 years after
the claim is not very useful
■ A flag capturing that back pain was listed as one of
the types of personal injury, and that a chiropractor
was engaged to treat the pain within 10 days can be
useful
■ In general, identify variables that based on experienced
SIU professionals raise “red flags” (“red flag variables”)
■ Immediate involvement of lawyer, certain types of
injuries, etc.
■ Derived variables such as unusual geographic
distance of medical care provider (e.g.,
chiropractor) from claimant home address
© Copyright StatSoft, Inc., 1984-2010. StatSoft, StatSoft logo, and STATISTICA are trademarks of StatSoft, Inc.
Approach:
Different fraud
models at first notice
of loss and then
rescoring the claim
each time more
information is
collected
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Why: The Result is the ability to quickly
generate an accurate Fraud Prediction on each
new and existing claim
■ Once Predictor Variables are identified, the relationships between them are
computed to create a formula, if for example the following variables:
■ A represents distance to therapy office from residence
■ B represents Attorney Involvement 1=Y, 0 = N
■ C represents soft tissue injury 1=Y, 0 = N
■ D represents loss date within 30 days of policy inception (value derived from
structured data)
■ E represents Targeted Pharmaceuticals involved 1=Y, 0 = N
■ N represents the remaining Predictors
■ The STATISTICA platform will generate a predictive fraud model based on your
data and experience that would look something like:
■ Fraud Score = .1A + .3B + .21C + .50D + .21E + (x)N…….
■ Then every bit of relevant (predictive) information collected on every claim
would be run against this model (i.e. data plugged into A….N), to determine the
claim’s probability of being fraudulent
■ A score is generated and then action taken based on your business rules (i.e. all
claims with fraud scores over 80 get referred to the SIU)
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LiveScore in Claim Workflow
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Predictive Claim Workflow Example
Initially this case looks like a
routine claim……
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Various Medical Bills Received and
other related expenses entered
I stayed home from my job as a teacher for one week. I had follow-up treatment with my family physician, Dr. Harvey Stein, six
days later. He told me to continue icing three times a day, and referred me to a physical therapist for my neck and back. I saw
Julie Lyons, RPT, for 4 weeks, twice a week, and then for 4 more weeks, once a week. I am still doing the stretching and
strengthening exercises at home. I’ve gone back to see Dr. Stein twice and have another appointment with him next week. I
still have quite a bit of pain in my neck and back.
My medical bills totaled $3,450 as follows (Copies of bills attached):
Ambulance: $650
Hospital E.R, x-rays, exam, neck brace: $490
Dr. Stein: $225
Julie Lyons, RPT: $1216
Prescriptions: Flexeril, Vicodin: $219
I have lost wages in the amount of $1000. (Documentation attached.)
As a result of the accident, I had to cancel reservations for a conference. The nonrefundable fee was $240. (Receipt
attached.)
As a result of being hit by Mr. Smith’s car, I couldn’t take my children to school and back for a week. I hired someone to help
with that for $75 (Receipt attached.) I also had to hire a cleaning person to take care of the house and I will continue to need
someone as long as I have pain in my neck and back. So far, this has cost me $600. (Cancelled checks attached).
© Copyright StatSoft, Inc., 1984-2010. StatSoft, StatSoft logo, and STATISTICA are trademarks of StatSoft, Inc.
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Claim Re-Scored after recent payment
request
IPC flag triggered from
Predictive model given recent
letter and related expenses to
reduce propensity to contact
lawyer
Text mining also invoked to
improve overall predictive
model accuracy to optimize
service levels
Goal: to provide the “right” level
of service given ever changing
circumstances
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SOFTWARE PRESENTATION
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22
To Identify new types of Fraud and Increased Fraud Model
Accuracy
TEXT MINING OVERVIEW
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23
Text Mining Summary
(Statistical Natural Language Processing)
■ Goal is to incorporate unstructured text into predictive modeling
■ Particularly well suited for fraud detection and estimating loss, from
■ First notice of loss, accident descriptions, adjustor notes
■ Emails, Letters, Faxes
■ Claim description
■ General approach is simple:
■ Narratives(PDF, Word, etc), adjustor notes extracted from your claims
database
■ Notes are pre-processed to correct spelling errors, etc.
■ Find and count phrases, words, etc. of a-prior interest or use statistical
methods to extract terms or phrases diagnostic of fraud, loss, etc.
■ “back pain”, “chiropractor”, “headaches”, soft tissue , …
■ Include word/phrase counts (incidences, transformed word frequencies) into
modeling
■ Automate (“deploy”) the text-mining “model” to score new text data (e.g.,
claims as they are filed)
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Text Mining: Illustration where a predictive fraud model is
created from the following text file
■ Text or Unstructured data extracted from claim documents file(s)
■ Raw data and extracted terms, model generated and fraud scores assigned
in batch or real time
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Effective Text Mining for Predictive
Modeling: Text Mining Details (1)
■ Example: Finding unusual narratives of aircraft accidents
■ Many “text-mining” approaches and solutions are geared towards finding
“common phrases” etc.
■ This is usually not
very interesting….
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Effective Text Mining for Predictive
Modeling: Text Mining Details (2)
■ STATISTICA Text Miner is optimized for:
Performance
(multithreaded indexing)
■ Easy deployment of
text models, for
efficient scoring
■
■ For example,
building models
for automatic
detection of
“unusual narratives”:
■
This can be
accomplished
through automatic
■ Latent semantic
indexing of claims
■ Identifying unusual or “very-usual” narratives that do not belong to any cluster
© Copyright StatSoft, Inc., 1984-2010. StatSoft, StatSoft logo, and STATISTICA are trademarks of StatSoft, Inc.
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Score Claim with new Text Data
Claim Narrative: 10-12-2010 Spoke with claimant and injury seems not to
have affected work or daily routine. Will pend for follow-up in 2 weeks.
New Claim note
added 10-12 Low
Fraud Propensity
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Two weeks later, new claim note added
Claim Narrative: 10-28-10 Spoke to claimant, told me that after
accident back was fine, yesterday went to Chiropractor and learned
that more treatments will be needed to treat the relieve the pain.
Also mentioned that friend told her that this pain could be
chronic and last for years and that she should talk to an
attorney.
New Claim note
added 10-28 now
Fraud Propensity
changes based on
scoring of new
information. Alert
generated to
refer claim to SIU
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BENEFITS AND CUSTOMER
EXAMPLES
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Example: Commercial Property &
Casualty Insurance Company
■ Background
ROI:
■ Commercial Property and Casualty Insurance
For one product line,
Company (auto, disability, property, etc., product a 800%+ expected
lines)
return in 1st year by
using text mining and
■ Predictive modeling in support of underwriting
data mining to
and fraud detection applications
uncover fraudulent
■ Applications
claims and
■ Underwriting: Actuaries use historical loss data opportunities for
to determine the factors driving claims risks and subrogation
develop predictive models of loss -> Agents use
applications that score policy applicants
■ Fraud detection: Actuaries build predictive
models to determine characteristics of fraudulent
claims -> As new claims are processed, the
models flag them for investigators
© Copyright StatSoft, Inc., 1984-2010. StatSoft, StatSoft logo, and STATISTICA are trademarks of StatSoft, Inc.
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Example: Workers Compensation
Insurance Company
■ Background
■ Disability and Workers’ Compensation Insurance
■ Predictive modeling in support of underwriting
■ Applied text mining to their claims analysis
■ Text mining of claims reports provides extra
accuracy in uncovering the key factors driving
historical losses
■ Underwriting Application and STATISTICA Live Score
■ Use Web-based application to support agents
writing policies
■ Models built using STATISTICA Data
Miner/STATISTICA Text Miner are deployed for
real-time scoring to STATISTICA Live Score
■ STATISTICA Live Score integrates with the Webbased application using Web Services
© Copyright StatSoft, Inc., 1984-2010. StatSoft, StatSoft logo, and STATISTICA are trademarks of StatSoft, Inc.
ROI:
• Increased accuracy
in policy underwriting
• Decreased IT costs
by migrating from inhouse scoring
application to
• Dramatically
reduced false
positives to SIU
•Next step:
Implementing
Predictive Claim flow
32
Thank you!
■ Overall impression? Can you see the value to Mercury Insurance? Now with
Predictive Modeling Combined with Text Mining now you can:
■ Catch fraud earlier, before to many claim payments sent
■ Identify problem claims earlier
■ Identify low touch, low complexity earlier to provide better service at
reduced cost
■ Identify new types of fraud, that were previously undetected
■ Any remaining questions?
■ Should we show to others, would you like a Software Presentation to reinforce
our outrageous claims of cost savings
■ Next Steps?
© Copyright StatSoft, Inc., 1984-2010. StatSoft, StatSoft logo, and STATISTICA are trademarks of StatSoft, Inc.
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History, Experience, Capabilities
STATSOFT
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STATISTICA Adoption
STATISTICA Rated Highest in Customer Satisfaction*
* 2010 Rexer Survey: Full report available at www.rexeranalytics.com
© Copyright StatSoft, Inc., 1984-2010. StatSoft, StatSoft logo, and STATISTICA are trademarks of StatSoft, Inc.
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Text Mining
STATISTICA Text Miner #1 Across Industries*
* 2010 Rexer Survey
© Copyright StatSoft, Inc., 1984-2010. StatSoft, StatSoft logo, and STATISTICA are trademarks of StatSoft, Inc.
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