U.S. Headquarters: StatSoft, Inc. 2300 E. 14th St. Tulsa, OK 74104 USA (918) 749-1119 Fax: (918) 749-2217 [email protected] [email protected]

U.S. Headquarters: StatSoft, Inc. 2300 E. 14th St. Tulsa, OK 74104 USA (918) 749-1119 Fax: (918) 749-2217 [email protected] www.statsoft.com

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data analysis data mining quality control web-based analytics

Insurance Fraud Detection: Reducing Loss PayoutUsing Predictive Modeling

Mark RuschVice President – [email protected]

© Copyright StatSoft, Inc., 1984-2010. StatSoft, StatSoft logo, and STATISTICA are trademarks of StatSoft, Inc. 2

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


Annual Premium $136MLoss Ratio 52%

Annual Losses Paid (est) $71M% loss due to fraud claims 5%

Annual losses due to fraud claims $3.5MFraud Claims Identified by PM 90%

Fraud Losses Identified by Model $3.2M

Projected Reduction on losses 4.5%

Annual Fraud Reduction ~$2MRevised Projected Loss Ratio 49.8%

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

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StatSoft WorldWide Offices & Sample Insurance Customers

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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


Predictive Analytics Enhances Many Insurance Processes

UnderwritingAutomated underwriting / risk selection

Straight-through rate processing

Active risk portfolio management

Automated discount/credit recommendation

Automated renewal processing

Underwriting fraud detection

Appetite selection management

Automated premium audit

MarketingCampaign optimization

Customer segmentation

1:1 marketing

New product market analysis

Outbound Predictive Marketing

Inbound intelligent cross sell

Optimize leads delivered to Agency Force

ClaimsFraud detection

Fast tracking of claims

Claims assignment automation (by competency)

Settlement analysis

Accelerated detection of severe claims

Predict Complexity

Routing optimization

Predict Reserves

Sales & ServiceField force optimization (marketing & agency)

Commission modeling and optimization

Cross-sell, up-sell, offer optimization

Intelligent call routing

Intelligent Recommendations

In and outbound Customer Retention offers

Enterprise Feedback Optimization

Agent /Broker Performance effectiveness


PREDICTIVE MODELING IN ACTION

Opportunities


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


Predictive Analytics can drive down claim costs at many points across the claim lifecycle

FirstReport

3 PointContact

Low Touch

SupervisorReview &

Assignment

Evaluation –Strategy and

Reserves

ManageClaim/MakePayments

NewInformation/

Medical Pharmacy Bill

AssignClaim

ReferralEscalation

Close

Nurse CaseManagement

Model Score

+ReasonCodes

Injury/Accident


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

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.


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

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.


FRAUD DETECTIONOpportunities


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!


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….


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


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, ……..


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

Approach:Different fraud models at first notice of loss and then rescoring the claim each time more information is collected


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)


LiveScore in Claim Workflow


Predictive Claim Workflow Example

Initially this case looks like a routine claim……


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).


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


SOFTWARE PRESENTATION


TEXT MINING OVERVIEW

To Identify new types of Fraud and Increased Fraud Model Accuracy


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)


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


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 notvery interesting….


■ 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

Effective Text Mining for Predictive Modeling: Text Mining Details (2)


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


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


BENEFITS AND CUSTOMER EXAMPLES


Example: Commercial Property & Casualty Insurance Company

■ Background

■ Commercial Property and Casualty Insurance Company (auto, disability, property, etc., product lines)

■ Predictive modeling in support of underwriting and fraud detection applications

■ Applications

■ Underwriting: Actuaries use historical loss data to determine the factors driving claims risks and 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

ROI:For one product line, a 800%+ expected return in 1st year by using text mining and data mining to uncover fraudulent claims and opportunities for subrogation

ROI:For one product line, a 800%+ expected return in 1st year by using text mining and data mining to uncover fraudulent claims and opportunities for subrogation


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 Web-based application using Web Services

ROI:• Increased accuracy in policy underwriting• Decreased IT costs by migrating from in-house scoring application to • Dramatically reduced false positives to SIU•Next step: Implementing Predictive Claim flow

ROI:• Increased accuracy in policy underwriting• Decreased IT costs by migrating from in-house scoring application to • Dramatically reduced false positives to SIU•Next step: Implementing Predictive Claim flow


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?


STATSOFTHistory, Experience, Capabilities


STATISTICA Adoption

STATISTICA Rated Highest in Customer Satisfaction*

* 2010 Rexer Survey: Full report available at www.rexeranalytics.com


Text Mining

STATISTICA Text Miner #1 Across Industries*

* 2010 Rexer Survey

Documents

U.S. Headquarters: StatSoft, Inc. 2300 E. 14th St. Tulsa, OK 74104 USA (918) 749-1119 Fax: (918) 749-2217 [email protected] [email protected]