IBM Counter Fraud Signature Solutions

IBM Counter Fraud Signature Solutions
November 5th, 2013 Athens
Carmen Ene, VP IBM Global Business Services , Europe Leader Counter Fraud & Financial Crimes
©2013 IBM Corporation
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
Member
DOS
HCPCS
Description
Units
Paid
12345678
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 conduct
Medical 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
• 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
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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• 20% of the cases were greater than 1M
• The frauds reported to us lasted a median
of 18 months before being detected
This new era is reshaping the IT landscape and creating new
market dynamics
The new era is driving the IBM Counter Fraud strategy
Cloud
Mobile
Social
Internet of Things
SEPTEMBER,
2013
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Helping our customers become a Hard Target
Predict and Protect
Disrupt and Defeat
Prosecute and Recover
Learn and Apply
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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
Organized rings
conducting
sophisticated attacks
against corporations
for producing financial
gains
 Staged Events
 Money
Laundering
 Improper Billing
 Improper
Payments
Providers taking
advantage of public
and private institutions
for the purpose of
improper financial
gain
Organized
Opportunistic
Individuals seeking
improper payments by
taking advantage of
private and public
institutions
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 Slip Fall
 Arson
 Tax Fraud
 Medical Fraud
 Procurement
 Financial
Statement
 Expense
Employees creating
fraudulent
transactions, records,
and claims to receive
improper payments
from Employers
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 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
Predict &
Protect
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
Detect
Prosecute &
Recover
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Gather data about DETECTED or DISCOVERED fraud; build cases for prosecution, recoveries, or denial of payments. Provide feedback to DETECTION and/or DISCOVERY
©2013 IBM Corporation
Disrupt &
Defeat
Take action in real time – when it matters
Detect fraud within a business process
Investigate
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Prevent
Fraudster
Confirm fraud for prosecution, recovery, rules and watch lists
Discover
Find fraud within the data
Learn &
Apply
Discover fraud retrospectively by reviewing past data and looking for patterns and anomalies that may indicate an individual or organization is potentially fraudulent
Counter-Fraud solutions must provide a layered approach by leveraging
multiple analytical techniques
Predictive Analytics
Entity Analytics
Forensic Analysis
Content Analytics
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Retrospective Analysis
IBM Fraud Solution Framework
Operational Systems
Advanced Industry Libraries:
Data Models, Predictive Models, Rules, Reports, Process, External Fraud Data and so on
Real Time / “In line”
Back Office Analytics
Prevention
Detection
Discovery
Investigation
Integration
Predictive
Analytics
Selection
Case
Management
Action
Evaluation
Rules
Guidance
Rules
Anomalies
Decision
Management
Identification
Relationship
Visualization
Investigative
Analytics
Reporting
Operational
Reporting
Dashboards
Feedback
Observation Space
Information
Domains
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©2013 IBM Corporation
Internal
Sources
External
Sources
Evolving
Unstructured
Sources
Fraud Use
Case Libraries
IBM Technology
Operational Systems
Advanced Industry Libraries:
Data Models, Predictive Models, Rules, Reports, Process, External Fraud Data etc.
Real Time / “In line”
Back Office Analytics
Prevention
Detection
Discovery
Investigation
Integration
Predictive
Analytics
Selection
Case
Management
Action
Evaluation
Rules
Guidance
Rules
Anomalies
Decision
Management
Identification
Relationship
Visualization
Investigative
Analytics
Reporting
Operational
Reporting
Dashboards
Feedback
Observation Space
Information
Domains
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©2013 IBM Corporation
Internal
Sources
External
Sources
Evolving
Unstructured
Sources
Fraud Use
Case Libraries
IBM Counter Fraud Solution
Form, Bill, Claim
Application, and so on
Business
Rules
Applicant
Discovery
Detection
Entity Analytics
Predictive
Model
Optimize
Fraud
Decisions
Entity
Analytics
Claimant
9,500 model
library
Anomaly
Detection
Selection
Evaluation
Identification
Real Time Alert
Provider
New Investigation
Observation Space
Case Management
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Intelligent Investigation
Intelligent Fraud
Dashboards
Public story: Fraud detection at Alameda County Social Services
Challenge:
Business Benefits:
Case workers were confronted with:
 Applicants applying for public assistance who were
not adequately screened prior to enrollment
 ROI: 631%, Payback 2 months, $24M annual
benefit
 Applicants receiving benefits that were noncompliant for several months to year
 Bring together data on child welfare system clients
from multiple payment and case management
systems
 Administrative caseload burdens of 300-600 per
worker, reducing their ability to spend time with
clients
 Decrease false positives and negatives and reduce
investigation time for increased fraud ROI
Solution:
 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
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©2013 IBM Corporation
- 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
Banking story: Preventing fraud at MoneyGram
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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©2013 IBM Corporation
Insurance story: Claim fraud detection at Santam Insurance in South
Africa
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.
Solution
Business Opportunity
 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.
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©2013 IBM Corporation
 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
Results
 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.
IBM Counter-Fraud and Financial Crime Signature Solution
An unparalleled combination of integrated capabilities, delivery experience, and
business expertise with a proven ability to deliver business outcomes
Learn and
Apply
Prosecute
and Recover
Disrupt and
Defeat
Predict and
Protect
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Predictive Police
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©2013 IBM Corporation
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