Fraud Prevention
Data Analytics and other Methodologies
Paul Crowder, FICO
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© 2014 Fair Isaac Corporation.
This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.
FICO Snapshot
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Profile
The leader in predictive analytics for decision management
Founded: 1956
NYSE: FICO
Revenues: $743 million (Fiscal 2013)
Products
and Services
Scores and related analytic models
Analytic applications for risk management, fraud, marketing
Tools for decision management
Clients and
Markets
5,000+ clients in 80 countries
Industry focus: Banking, insurance, retail, health care
Recent
Rankings
#1 in services operations analytics (IDC)
#6 in worldwide analytics software (IDC)
#7 in Business Intelligence, CPM and Analytic Applications (Gartner)
#26 in the FinTech 100 (American Banker)
Offices
20+ offices worldwide, HQ in San Rafael, CA
2,200 employees
Regional Hubs: San Diego (CA), New York, London, Birmingham (UK),
Toronto, Johannesburg, Munich, Madrid, Sao Paulo, Bangalore, Beijing,
Singapore
© 2014 Fair Isaac Corporation.
© 2012 Fair Isaac Corporation.
Payment Integrity
A Range of Approaches
“Crawl”
“Walk”
“Run”
“Fly”
Fraud, Waste
& Abuse
(FWA) are
ID’d by
chance.
FWA are ID’d
post-payment
with Enterprise
BI
FWA are ID’d
post-payment
via predictive
analytics.
Strategy &
process may
be formal, or
ad hoc.
There is a
formal PI
program of
strategy,
process &
people.
FWA are ID’d
pre- and postpayment via
rules &
predictive
analytics.
A limited
program of
strategy,
process, or
people.
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© 2014 Fair Isaac Corporation.
People are
otherwise
engaged.
There is a
sophisticated
PI program of
strategy,
process &
people.
FICO Client Case Study 1
Provider Abuse + Systemic Weakness
A single claim line (CPT 99070, Supplies
and Materials), approved for US$259
payment, scored high for Procedure Rate
(unusually rapid repetition over time).
On review, this claim line should have
been automatically denied during autoadjudication, as it was for an expense that is
considered to be part of Provider overhead
expenses.
► The
Scheme discovered that it was paying all of their expense
procedure codes in conflict with the Scheme’s established policy.
► Further,
the Provider is discovered to be acting with intent.
► The
US$259 high scoring claim became a US$1 million case with
part-time effort by one investigator over a 2 week period … 100:1
ROI.
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© 2014 Fair Isaac Corporation.
The Power of Data-Driven Predictive Analytics
Queries/Rules
Simple schemes and billing errors
Known fraud and abuse patterns
Predictive/Data-Driven
Analytics
Queries/Rules benefits above
AND
Complex fraud and abuse patterns
Undiscovered fraud
New and emerging issues
Organized Fraud
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© 2014 Fair Isaac Corporation.
FICO Client Case Study 2
Clinically Unnecessary Care
A dentistry provider scored high (aberrancy) for 5 reasons,
including “High Member Day”
► Peers
► The
averaged US$195 per member per day
suspect averaged over US$1,700 per member per day
► Findings
►Stainless
Steel Crowns were routinely installed on every tooth of
every child treated by this provider.
►Clinically unnecessary in every case.
►Multiple provider identities
►3 years of abuse.
► Result
►A US$3
million case
►Imprisonment of the provider
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© 2014 Fair Isaac Corporation.
Preventive Measures
► Credential
► Employ
(review) providers before you admit them to your scheme.
a strong claims adjudication system.
► Score
claims for aberrancy, post adjudication/pre-payment, manually review
high scoring (suspicious) claims, & don’t pay the claims that you
shouldn’t pay.
► Use
integrated “force multiplier” technologies such as decision
management software, Link Analysis & Investigational Case Management
to rapidly review & decision findings, ID broader suspicious patterns & build
prosecution-ready case documentation.
► Use
pre- and post-payment findings to strengthen your claims
adjudication results, & take action on identified systemic weaknesses and
policy gaps.
► Application
of (new) preventive measures is a change in process …
success depends upon the strength of your relationships with your
internal & external peers, customers, members & stakeholders.
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© 2014 Fair Isaac Corporation.
FICO Client Case Study 3
Mathematics are the Universal Language
► The
Client: A Dutch Dentistry Scheme
►1.2 million beneficiaries
►€101 million paid Dental claims per year
► The
Engagement: A for-fee FICO IFM Analytic Assessment
►FICO scoring of 3,200 Providers using 12 months of paid claims
data.
►FICO Delivery of results that are “blind,” due to a language
barrier between FICO’s analytic scientists & the client’s data.
► The
Results:
►106 (3%) aberrant Providers. In the top 30, ID of all 12 known
fraudsters & 12 new suspects.
►€$15 Million Savings from review of 20 high scoring dentists - 14
were found fraudulent (70% Hit Rate).
►250% ROI projected for Year 1 of the predictive analytics solution.
► The
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Conclusion: Mathematics is the universal language.
© 2014 Fair Isaac Corporation.
The Recommended Approach for the Medical Schemes
► Rules
that target known types of fraud and abuse
► Example:
Claim System Edits
► Unsupervised
models that score paid claims and
providers to detect known, unknown and emerging
problems pre- and post-payment
► Vigorous
pre- and post-pay workflow for
scoring/detection, review and investigation.
► Integrated
software that maximizes efficiency
and that makes analytic results actionable is key.
► Strong
relationships with internal and external
peers, customers, members and stakeholders.
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© 2014 Fair Isaac Corporation.
FICO Client Case Study 3
Providers Have Bills to Pay Too
► The
Client: A commercial Medical Aid Scheme who
scores claims daily, post-adjudication, but reviews
claims “quick post-payment.”
► The
Initial Claim Scoring Result: A claim line which
ordinarily pays at US$250 scores high for High Paid
Claim (an unusually high amount paid for the
procedure) … at US$25,000 paid.
► The
Initial Explanation: “A clerical error.”
► On
Further Review: Submittal and payment for the
same US$25,000 procedure, one time in each of the
previous 2 years.
► The
Finding: The provider was manipulating the
Scheme’s auto-adjudication system, one time each
year, for payment of his child’s university tuition bill.
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© 2014 Fair Isaac Corporation.
The Rise of Auto-Adjudication
► Who
► If
processes their claims manually?
not now, soon:
Nobody
► Advantages
of Auto-Adjudication
► For
the Providers: Quick Payment
► For the Members: Limited level of involvement
► For the Medical Aid Scheme, payment of:
► The
correct claims
► For the correct amount
► For your members
► For providers who are authorized to participate in your plan.
► The
Problem with Auto-Adjudication: You will pay claims that, by
policy, contract or design, should not be paid.
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© 2014 Fair Isaac Corporation.
FICO Client Case Study 4
Technology: Good, Technology: Bad
► The
Client: A commercial Medical Aid Scheme who
successfully promoted the use of “Baxter” machines for
drug dispensing
► More
accurate dispensing for chronic conditions
► Lower dispensing fees for use of automated dispensing
► The
Initial Claim Scoring Result: Numerous
pharmacy claims scoring high for Duplicate Class, Rate
and Excess Days
► The
Findings:
► Unnecessary
normal dispense on the same day as the first
Baxter fill.
► Normal + Baxter dispense to mask excessive dispensing of
narcotics
► Excess supplies not prevented with use of Baxter
► Claiming for normal dispense of drugs that should have
been (or were) dispensed with Baxter
Results: €1.4 million in annual savings via
clarification of policy & policing of abusive providers.
► The
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© 2014 Fair Isaac Corporation.
Questions?
Confidential. This presentation is provided for the recipient only and cannot
be reproduced or shared without Fair Isaac Corporation's express consent.
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© 2013 Fair Isaac Corporation.
THANK YOU
Paul Crowder
Pre Sales Consulting, FICO
paulcrowder@fico.com
Confidential. This presentation is provided for the recipient only and cannot
be reproduced or shared without Fair Isaac Corporation's express consent.
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© 2013 Fair Isaac Corporation.