Fraud and Forensics:

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© 2010 IBM Corporation

© 2010 IBM Corporation

August 10, 2010

Fraud and Forensics:

New Techniques, Better Results



National Association of State Auditors, Comptrollers, and Treasurers

© 2010 IBM Corporation


Fraud, waste, and abuse costs governments hundreds of
billions in revenues each year


Medicaid fraud increasing throughout state, but
economy forcing cutbacks in investigations… losses
could be
$2 billion

a year
.”
Naples (FL) Daily News, 1/4/2009

Medicare &
Medicaid

Tax &
Revenue

Worker’s
Compensation

Unemployment
Insurance

Food & Nutrition
Programs

“…
the FTB has pegged California’s tax gap
associated with the PIT and CT at
$6.5 billion

annually

.”
California Legislative Analyst’s Office, 2008
-
09 Budget

“…
workers’ compensation fraud is the fastest
growing insurance scam in the nation. Today, 10
cents out of each premium dollar is wasted on
fraud
.”
Connecticut Conference of Municipalities, 11/12/2008

“…sophisticated shell games are costing Michigan's
unemployment trust fund up to an estimated
$95
million

a year.

.”
Michigan Unemployment Insurance Agency

“Every year, food stamp recipients exchange
hundreds of millions of dollars

in benefits for cash
instead of food with retailers across the country...”
Lincoln (NC) Tribune, 10/21/2006

Improper
Payments

“…agencies reported a total improper payment
estimate of about
$55 billion

for fiscal year 2007
.”
US Government Accountability Office, 1/23/2008

© 2010 IBM Corporation


Date provided to OMB by the Federal Departments and Agencies
.

Fraud, waste, and abuse costs governments hundreds of
billions in revenues each year

© 2010 IBM Corporation


The problem of fraud and abuse has process, organization,
technology, and analytics dimensions

Fraud &

Abuse

Process

Organization

Analytics

Process Dimension


Are proper business controls and
procedures in place to detect and deter
fraud and abuse?


Do laws and policies constrain fraud
related processes?


Are fraud detection and investigation
processes optimally aligned to
organizational goals?

Organization Dimension


Are there organizational barriers to
implementing an effective fraud
detection and investigation
program?


Is there a single focal point (person
or team) that is responsible for
fraud and abuse activities?


Are key performance indicators in
place that measure and promote
excellence in fraud and abuse
pursuit?

Analytics Dimension


What detection analytics, if any,
are in place?


Do detection analytics allow for
both immediate and
retrospective analysis?


Are analytics used to control
and optimize fraud workload?

Technology

Technology Dimension


What technologies are used to
support fraud detection and
investigation?


Does fragmented data create
challenges in having a complete
picture of behavior?


Does technology support
measurement and reporting of
fraud exposure?

© 2010 IBM Corporation


Predictive
Models


Who is behaving well?


Which entities are likely to behave “badly” in the
future?


What are the indicators that an entity’s behavior
is getting “better” over time? “Worse” over
time?

Data Mining &
Clustering


What are patterns of non
-
compliant (and
criminal) behavior that I don’t know about?


If I catch a “bad” entity, how can I find out who
else is behaving like that?


Are there groups of entities who behave the
same way?


Which entities are behaving differently than
others (in a suspicious way)?


How “good” or “bad” is a entity behaving,
relative to other entities?


What is “normal” behavior?

Outlier
Detection

Analytics are transforming how governments are tackling
fraud, waste, and abuse

© 2010 IBM Corporation



New York State Department of Taxation and Finance


Income tax refund fraud and abuse


Debt collection



North Carolina Department of Health and Human Services


Medicaid fraud detection

Case Studies

© 2010 IBM Corporation



5,000 total employees


Approximately $60 billion collected annually (2009)


Highly sophisticated taxpayer population (most Fortune 500
companies have a presence in New York)


Wide range of

taxpayers (demographic and cultural)

New York State Department of Taxation and Finance

© 2010 IBM Corporation


The project objective was to build a system to enhance existing audit
case selection methods for fraud detection of pre
-
processed Returns

The Questionable Refund Detection unit wanted…


A better way to identify questionable returns


To question suspect returns before issuing refunds


To improve collect ability of audit cases


To issue refunds in a timely manner


To make program management more flexible


To leverage investments in data warehousing and business intelligence
technologies


To scientifically predict good audit candidates utilizing return filing patterns, case
history, and other external indicators


To improve their ability to detect new areas of fraud

New York State Department of Taxation and Finance

© 2010 IBM Corporation


CASE STUDY


State of New York

Predicting tax compliance


The Case Identification and Selection System (CISS) applies
business rules and predictive models to categorize and score
returns nightly and identifies the ‘next best case’ for audit
selection. In addition, a separate web based portal provides
screening and resolution of cases.

Solution

Benefits

Challenge

New York wanted to enhance current audit case selection
methods for detection of audit issues at the time a return is
processed. Specific audit programs include Earned Income
Credit, Dependent Child Care Credit, Itemized Deductions,
Wage/Withholding, and Identity Theft.


$889 million

increase in revenue in the first five years


Increased screener and auditor productivity


Enhanced taxpayer correspondence


Improved audit program management

9

© 2010 IBM Corporation



100 employees in Program Integrity Unit


Approximately $14 billion in annual paid claims (2009)


$25 million in recoupment letters issued annually

North Carolina Department of Health and Human Services

© 2010 IBM Corporation


Solution components:


IBM Fraud Analysis Center


InfoSphere Identity Insight


IBM Global Business Services:
Business Analytics and
Optimization


IBM Software Group: Lab
Services

“I think we are going
to save tens of
millions of dollars.”



Beverly Perdue, Governor
State of North Carolina

The Need:

This large state social services agency faces a significant exposure
to healthcare fraud and abuse. The current business process and
technology used to fight fraud, waste, and abuse in Medicaid is
ineffective


producing only around $25 million annually in recoveries.
This leakage, combined with a significant state budget deficit,
motivated the state to aggressively pursue cost takeout projects.

The Solution:

The state implemented a comprehensive health analytics solution.
This solution examines claims for suspicious patterns of behavior,
quickly identifying providers and recipients for investigation. In
additional, the solution identifies organized criminal rings and
collusive behaviors by uncovering suspicious relationships among
providers and between providers and recipients.

Benefits:



$60m
-

$100m

in recoupments in a 12 month period (expected)

CASE STUDY


State of North Carolina

Detecting and pursuing Medicaid fraud

© 2010 IBM Corporation


Claim Data Identified $140M = 18% in suspect claims

Personal Care Services ($86M of $555M = 15%)


Schemes identified within PCS include:

High payments per patient, high home health aid visit utilization

Billing for services on Sundays/Holidays that may not have been
rendered

Out of sequence billing

Durable Medical Equipment ($55M of $235M = 23%)


Schemes identified within DME suppliers include:

Expensive orthotics for amputees

Respiratory equipment unbundling



North Carolina Department of Health and Human Services

© 2010 IBM Corporation


PCS Analysis Results

North Carolina Department of Health and Human Services

© 2010 IBM Corporation


DME Investigation
-

Provider 1122233


Andy Griffith Medical Equipment


Andy Griffith Medical Equipment claims reveal the following patterns:


Claim submission delay which may indicate bill fabrication


Many patients with only 1 visit or service and over $5,000 charged


High reimbursement per patient in traction equipment, beds and power
wheelchairs


Billing high charges for only a short window of time


appears to be a
store front scheme




Relationship analytics discovered that these providers are related to
Andy Griffith Medical Equipment:


112244, 112255, 112266
-

each or these DME suppliers exhibit the
same behaviors


Biilling for items for the same patients in the following pattern:


1 DME supplier bills the wheelchair


2 months later, another of the suppliers bills repair and additional
accessories




© 2010 IBM Corporation


CASE STUDY


Predicting the “next best”
case and technique for debt collections

The Collections Optimizer applies business rules and
predictive models to prioritize the ‘next best debt’ for
collection. The solution will also create a customized
“collection technique map” for each individual collection
case, rescoring as new events occur.

Solution

Benefits

(expected)

Challenge

New York assigns collections cases based on dollar value
and uses a standard series of techniques to pursue
payment. This approach has led to a substantial backlog of
collections cases. As new events occur that affect
collectability, cases are not reprioritized. As a result,
collectors may spend time on cases that have low
probability of collection.


$100 million

increase in revenues collected over a 3 year
period


Substantial reduction in backlog of delinquent debts owed
to the state

15

New York State Department of Taxation and Finance

© 2010 IBM Corporation


What lessons can you take away from these case studies?

1.
We are in the “early adopter” phase of sophisticated, real
-
time analytics in government

2.
Learn more about these methods and technologies


Helps to provide proper oversight


Can recommend proactively in your audits

3.
Analytics uses math and statistics to examine your existing data sources smarter, faster, and
better

1.
Not an exercise in “magical” data sources

2.
Privacy concerns (if any) can be overcome

4.
Using analytics is plain ol’ good government


Great approach for helping to close budget gaps and be responsible stewards of public funds


Can enable positive ROI in same budget cycle


Allows you to do more with the same or less


Sentinel effect

5.
Adopt analytics in YOUR audits and day
-
to
-
day work

6.
Ways to get started


Pick a business risk and jump in


Government Commission


Fraud Club



© 2010 IBM Corporation


Frieda Yueh

fyueh@us.ibm.com

(914) 474
-
6606


Shaun Barry

shbarry@us.ibm.com

(516) 203
-
6063

Contact Information