ibm counter fraud solution

©2013 IBM Corporation

IBM Counter Fraud Signature Solutions

November 5th, 2013 Athens

Carmen Ene, VP IBM Global Business Services , Europe Leader Counter Fraud & Financial Crimes


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Provider ID Theft

o Claim for routine services is submitted by Dorsey Med Group located at 2625 Piedmont Road Northeast, Suite 56-331

o The provider is listed as Dr. Harry Dorsey, a licensed internist in the State of Georgia

o Dorsey is a respected physician with 39 years of experience.

o However…

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Provider ID Theft

o 2625 Piedmont Road Northeast, Suite 56-331 is a UPS Store mail box

o Dr. Harry Dorsey’s practice in Albany Georgia - 200 miles away

o The business was incorporated by Olga Teplukhina who also applied for the NPI

o When contacted, Dr. Dorsey did not have any knowledge of Dorsey Med Group

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Excessive Units

o Provider submits a claim for a patient suffering for post surgical nausea consisting of 200 “units” of Tigan (HCPCS J3250)

o One "unit" of Tigan is 200mg

o The maximum daily dosage is 800mg per day (4 units)

o This could be a mistake that happens as the result of confusion over the dosage (200mg) versus units

o Typical dosage cost is less than $10

o However…

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Excessive Units

o One provider was identified as an outlier that billed every instance at 200 units

o In most cases, the provider is paid approximately $900 per dose (versus $10 per dose, which is normal)

o Some patients had multiple visits - as many as 70 in one quarter

o Over a period of 10 months, the provider received approximately $2,000,000 related to the alleged administration of Tigan

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Member DOS HCPCS Description Units Paid12345678 5-1-2011 J3250 Trimethobenzamide 200 $900


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Geospatial and Cluster Analysis

o We uncovered abnormal billing related to HCPCS code E0172 (Seat lift mechanism placed over or on top of toilet)

o The behaviors we identified were material, distinct, and by definition, anomalous

o Total disbursements for this device exceed $230,000 for the period of January-July 2011

o Payments were highly concentrated for a large number of patients in a remote regional in Texas

o The listed addresses were highly suspect and consisted of a video store, a deserted home, and an empty strip mall

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IBM is seeing an increase in fraud that is expected to continue. We anticipate greater focus on fraud as a potential compromise in closing budget gaps

1. Crime rings are increasingly turning to fraud Fraud is low risk and relatively easy to conductMedical claims are path to revenue for fraudsters

2. Economic downturns lead to greater fraud and abuse

Individuals and businesses seek new ways to make ends meet

3. Market conditions pressuring our public and private bottom line

Need to find new sources of savings

4. Advances in analytics are make finding and preventing fraud both possible & economical

We can now do what we previously couldn’t

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• Survey participants estimated that the typical organization loses 5% of its revenues to occupational fraud each year

• The median loss caused by the occupational fraud cases in our study was $140,000

• 20% of the cases were greater than 1M

• The frauds reported to us lasted a median of 18 months before being detected


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Mobile

Cloud

This new era is reshaping the IT landscape and creating new market dynamics The new era is driving the IBM Counter Fraud strategy

Social

Internet of Things

SEPTEMBER, 20138


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Learn and Apply

Disrupt and Defeat

Predict and Protect

Prosecute and Recover

Helping our customers become a Hard Target


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Individuals seeking improper payments by

taking advantage of private and public

institutions

Organized rings conducting

sophisticated attacks against corporations

for producing financial gains

Fraud (aka Improper Payment or Program Integrity) is a deliberate misrepresentation or deception intended to result in financial gain. Fraud is a criminal act. Abuse refers to similar actions not proven to be criminal. Financial Crimes includes Anti-money laundering and cyber-risk primarily for banking

OpportunisticOrganized

Providers taking advantage of public

and private institutions for the purpose of improper financial

gain

Employees creating fraudulent

transactions, records, and claims to receive improper payments

from Employers

Staged Events Money

Laundering

Improper Billing Improper

Payments

Slip Fall Arson Tax Fraud Medical Fraud

Procurement Financial

Statement Expense


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Anatomy of a “Complex” fraud


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IBM’s Counter Fraud solution reduces improper payments using a layered “best of breed” approach to disrupt the intentions of both organized and opportunistic fraudsters

Detect fraud within a business process

Take action in real time – when it

matters

Find fraud within the data

Confirm fraud for prosecution,

recovery, rules and watch lists

PreventDetect

Investigate DiscoverFraudster

Learn & Apply

Disrupt & Defeat

Predict & Protect

Prosecute & Recover

Apply the results of Detection to stop processing known fraud, or encourage fraudsters to abandon their objective by showing more is known than they think should be known

Discover fraud retrospectively by reviewing past data and looking for patterns and anomalies that may indicate an individual or organization is potentially fraudulent

Gather data about DETECTED or DISCOVERED fraud; build cases for prosecution, recoveries, or denial of payments. Provide feedback to DETECTION and/or DISCOVERY

Detect in real time if a medical bill or other transaction is fraudulent by applying models and rules in real time to determine the propensity for fraud

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Counter-Fraud solutions must provide a layered approach by leveraging multiple analytical techniques

Retrospective Analysis

Predictive Analytics

Forensic Analysis

Entity Analytics

Content Analytics


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IBM Fraud Solution Framework

InformationDomains

Internal Sources

External Sources

Evolving Unstructured

Sources

Fraud Use Case Libraries

Operational Systems

Advanced Industry Libraries: Data Models, Predictive Models, Rules, Reports, Process, External Fraud Data and so on

Prevention InvestigationReporting

Discovery

Integration

Action Operational Reporting

Guidance

Rules

Dashboards

Feedback

Case Management

Selection

Relationship Visualization

Evaluation

Identification Investigative Analytics

Observation Space

Detection


Rules

Decision Management

Anomalies

Real Time / “In line” Back Office Analytics


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IBM Technology

InformationDomains

Internal Sources

External Sources

Evolving Unstructured

Sources

Fraud Use Case Libraries

Operational Systems

Advanced Industry Libraries: Data Models, Predictive Models, Rules, Reports, Process, External Fraud Data etc.

Prevention InvestigationReporting

Discovery

Integration

Action Operational Reporting

Guidance

Rules

Dashboards

Feedback

Case Management

Selection

Relationship Visualization

Evaluation

Identification Investigative Analytics

Observation Space

Detection


Rules

Decision Management

Anomalies

Real Time / “In line” Back Office Analytics


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Business Rules

Entity Analytics

PredictiveModel

OptimizeFraud

DecisionsAnomalyDetection

DetectionForm, Bill, ClaimApplication, and so on

Intelligent InvestigationIntelligent Fraud

Dashboards

Entity Analytics

IBM Counter Fraud Solution

Case Management

Real Time Alert

New Investigation

Claimant

Provider

Discovery

9,500 model library

SelectionEvaluation

Identification

Observation Space

Applicant


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Public story: Fraud detection at Alameda County Social Services

Case workers were confronted with: Applicants applying for public assistance who were

not adequately screened prior to enrollment Applicants receiving benefits that were non-

compliant for several months to year Administrative caseload burdens of 300-600 per

worker, reducing their ability to spend time with clients

Alameda County implemented a new Social Services Integrated Reporting System (SSIRS) powered by IBM InfoSphere Identity Insight, IBM BI Data Warehouse and Cognos Reporting,Charting, and Dashboarding Services were provided by IBM 200 Concurrent Users interact with SSIRS through

web enabled interfaces

Challenge: Business Benefits:

ROI: 631%, Payback 2 months, $24M annual benefit Bring together data on child welfare system clients

from multiple payment and case management systemsDecrease false positives and negatives and reduce

investigation time for increased fraud ROI- Investigators now receive high ROI case alerts- Workers are alerted to child and adult endangerment,

“double dipping”, and fraudulent representation- Investigators receive relationship information

immediately

Solution:


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Banking story: Preventing fraud at MoneyGram

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MoneyGram

“Since the tool launched in May 2010 as part of MoneyGram’s efforts to enhance its global consumer anti-fraud program, MoneyGram has prevented thousands of fraudulent transactions, saving its customers about $22.5 million.”

Business Wire 03/09/11


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Insurance story: Claim fraud detection at Santam Insurance in South Africa

Solution

Business Opportunity

Results

South Africa’s largest short-term insurance company uses predictive analytics to uncover a major insurance fraud syndicate, save millions on fraudulent claims and resolve legitimate claims 70 times faster than before.

Gained the ability to spot fraud early with an advanced analytics solution that captures data from incoming claims, assesses each claim against identified risk factors and segments claims to five risk categories, separating higher-risk cases from low-risk claims. Plans to use propensity modeling to enhance and refine

segmentation process as more data becomes available Like most insurers around the world, Santam was

losing millions of dollars paying out fraudulent claims every year. Expenses were being passed on to the customer

in the form of higher premiums and longer waits to settle legitimate claims. To improve its bottom line and enhance customer

satisfaction, the company needed to detect and stop insurance fraud early in the claims process. It also needed to find a way to isolate risky,

fraudulent claims so that claims managers could more quickly process lower-risk claims.

Identified a major fraud ring in less than 30 days after implementation. Saved more than $2.5M in payouts to fraudulent

customers, and nearly $5M in total repudiations. Reduced claims processing time on low-risk claims by

nearly 90%. Cut operating costs by reducing the number of mobile

claims investigations.


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An unparalleled combination of integrated capabilities, delivery experience, and business expertise with a proven ability to deliver business outcomes

Prosecute and Recover

Predict and Protect

Disrupt and Defeat

Learn and Apply

IBM Counter-Fraud and Financial Crime Signature Solution


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Predictive Police


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