white paper
FICO™ Insurance Fraud Manager—
Health care Edition
Before and After Payment—
Reducing Losses by Improving Decision Insight
www.fico.com Make every decision countTM
FICO™ Insurance Fraud Manager
»» Introduction
Decisions Drive Health care Cost and Quality
The health care industry has a powerful new ally in its continuing battle to control costs and improve
care. Advanced decisioning systems—combining data, analytics and automation—help health care
payers and providers precisely answer tremendously complex questions such as:
• Should I insure this individual?
• Should I pay this claim?
• Should I investigate this billing anomaly?
• Should I prescribe this drug?
• Should I market to this prospect?
These types of decisions affect health care cost and quality every day. If organizations can make them
better and faster, they can improve care, increase process efficiency, eliminate unnecessary expense,
waste and loss, and operate more profitably. Case in point: the Government Employees Hospital
Association (GEHA), a 450,000-member federal employee health plan and early adopter of advanced
decisioning, achieved 3:1 ROI within the first year.
How exactly do advanced decisioning systems make a difference? They enable complex decisions to be
made with a very high degree of accuracy—and in the context of all relevant information and policies—
very rapidly. Even in real time.
In many cases advanced decisioning means applying numerous, intricate policy rules to decisions in a
precise and consistent manner. For example, automated rule-based systems can analyze prescriptions
from multiple physicians treating the same patient and generate alerts to potentially dangerous
drug interactions. Rule-based systems can also enable physician’s assistants to take on greater
responsibility during appointments, ensuring that they collect all required historical and symptomatic
information according to prescribed protocols.
Advanced decisioning may also involve using mathematical models to perform automated, in-depth
analyses of data drawn from many sources. Analytic models of this sort have long been used in the
health care industry in such areas as disease management. Today they are moving into more and
more areas of operations. Applications include reducing losses from billing mistakes, fraud and abuse,
improving product development plan designs, developing optimal pricing strategies, identifying
cross-selling opportunities and increasing customer retention.
Outputs from these decisioning processes—in the action-ready form of scores, explanations and
recommendations—guide human experts in making decisions. Models can generate scores, for
example, indicating the likelihood a health care claim is fraudulent. Rules can then be used to
automatically route all claims scoring above a specified threshold into the queues of investigative
units. Analytics and rules can also rank-order claims by their score and provide explanations for why
claims scored as they did, focusing investigative personnel on the riskiest claims and giving them a
place to start their work.
This white paper looks at the application of an advanced decisioning system, FICO™ Insurance
Fraud Manager, to health care claims payment and investigation. It shows how this state-of-the-art
system—with specific solutions for medical, dental and pharmacy payers—improves operational
efficiency and cuts expense across the full length of the claims spectrum, from prepayment claims
processing through postpayment loss recovery.
© 2011 Fair Isaac Corporation. All rights reserved.
page 2
FICO™ Insurance Fraud Manager
»» FICO™ Insurance Fraud
Manager—Better
Decisions at Every Stage
of Claims Management
Insurance Fraud Manager is the FICO solution for detecting and addressing billing and policy errors,
and fraud and abuse in health care claims. Insurance Fraud Manager is the first system of its kind—the
only system on the market today—that performs this full range of detection:
1. Prepayment scoring. Insurance Fraud Manager identifies problems with a claim before the check
is cut. Overnight or even in real time, it analyzes and scores individual claims according to degree
of risk for fraud and abuse while catching billing errors.
2. Fast-cycle checkpoints. Insurance Fraud Manager reduces losses by providing early
warnings about the riskiest providers. Weekly or within other frequent timeframes, it statistically
aggregates a year’s worth of prepayment scores for each provider, then ranks providers by total
level of risk.
3. Postpayment analysis. Insurance Fraud Manager detects large-scale patterns of fraud and abuse
as well as emerging fraud schemes. Annually or within other extended timeframes, it performs
complex, in-depth analyses of large batches of claims, detecting patterns of fraud and abuse that
would not be evident in smaller datasets.
This powerful solution for health care payers uses a combination of patented profiling technology,
predictive models, statistical analysis and rules to achieve a level of detection accuracy unmatched by
any other method. The technology is similar to that used by the credit card industry to analyze each
and every card transaction in the context of other purchases (including those a cardholder made
minutes before and those extending years back). This technology, which enabled the US credit card
industry to reduce its fraud loss rate by two-thirds (from 18 basis points in 1992 to 6 basis points in
2003)1, now manages 65 percent of the world’s credit card transactions.
Insurance Fraud Manager is available for government and commercial health care. It can be
configured with models built around the characteristics, processes and billing practices of medical,
dental or pharmacy payers.
Prepayment Scoring
The American Medical Association reports that it has been successful in passing prompt payment
legislation in 47 states and is actively lobbying for the same in the additional states, as well as for
sharper enforcement teeth in all regulations.2 Today health care payers have a very narrow time
window in which to process claims if they are to avoid fines.3 As a result, health care insurers are
forced to pay, alongside the majority of legitimate claims, a substantial number of claims that should
not be paid. Studies show that only pennies on the dollar are recouped after the fact,4 and the
process of trying to retrieve these funds is time-consuming, labor-intensive and expensive.
When used in prepayment mode, Insurance Fraud Manager decreases the volume of payments
made on erroneous, fraudulent and abusive claims by immediately identifying problems like
near-duplicates, upcoding, adjudication errors, unbundling, OCR scanning errors, unit inflation and
payment policy weaknesses. The result is improved payment integrity—confinement of payments to
correct and legitimate claims—without slowing processing.
1. Nilson Report and Lafferty Research; basis points based on every $100 card charge
2. American Medical Association ,“Sick and tired of providing interest-free loans?,” June 2002
3. In Texas alone, health plans have paid out over $38 million dollars in fines for noncompliance with state prompt payment laws
since August 2001.
4. The Department of Health and Human Services and The Department of Justice Health Care Fraud and Abuse Control Program
Annual Report For FY 2002, http://www.usdoj.gov/dag/pubdoc/hcfareport2002.htm
© 2011 Fair Isaac Corporation. All rights reserved.
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FICO™ Insurance Fraud Manager
As shown below, this added protection can be invoked at the moment the claim is received.
FICO™ Insurance Fraud Manager can operate in real time, instantly analyzing each claim individually
to detect errors and predict the likelihood of fraud and abuse. (Pharmaceutical claims are typically
analyzed in this way.) Alternatively, Insurance Fraud Manager can analyze claims in batches several
times a day or overnight, and still deliver the results prior to payment.
THE Health care CLAIMS PAYMENT SPECTRUM
Incoming
Claims
$
Prepayment
Seconds, minutes, hours
Real-time scoring
Catch more billing errors, including near-duplicates rules miss; focus attention immediately on serious
fraud and abuse; avoid pay and chase; reduce the number of legitimate claims pended for review
Batch scoring
Insurance Fraud Manager is able to perform complex analyses in real time because it creates a
comprehensive view of each entity (provider, patient, etc.) in the form of an extremely compact data
object. It then stores these data objects in memory, making them instantly accessible to prepayment
models and minimizing the need for database queries.
Using these data objects, Insurance Fraud Manager analyzes each claim based on much more than
just the information on the claim itself. Claims are analyzed in context—deep historical context
combined with the broad context of activity across the health care network. For example:
• Current and historical data about the patient
• Current and historical data about other patients receiving similar services
• Data about the provider(s) associated with the claim, including their history of providing care
Context analysis is important since many fraudsters know how to submit claims that are, on face
value, correct. With more and more claims being auto-adjudicated, fraudsters have learned that
they no longer need to get by an adjustor. As long as they submit a clean claim, where procedures
match diagnosis, and get the age and gender of the patient right, it’s likely to pass through the edit
software. More clever fraudsters submit claims that fit the typical practice patterns of the specialty for
which they are submitting claims. Some even test the parameters of detection, discovering through a
carefully planned series of test submissions which combination of factors enable a claim to fly below
the radar of a processing operation.
But Insurance Fraud Manager is not so easily fooled, since it examines the claim simultaneously
with other data and looks at complex interrelationships among this data. It can find near-duplicates
(where changing a procedure modifier on one of a pair of otherwise identical claims is enough to
pass through the claims edit logic that detects duplicates).
© 2011 Fair Isaac Corporation. All rights reserved.
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FICO™ Insurance Fraud Manager
While these sophisticated analytics detect more error, fraud and abuse, they also reduce the number
of legitimate claims diverted for review, thereby reducing the load on special investigation units (SIUs)
and improving investigative productivity and recovery results. Other systems, for example, might flag
every claim above a certain dollar amount from providers whose claims have been suspect before—
sweeping up many bills unnecessarily and thereby wasting investigative resources and risking latepayment fines. Using FICO™ Insurance Fraud Manager’s advanced rules capabilities, however, payer
organizations can create far more refined filters—flagging only those claims with the particular set of
procedure codes or diagnoses, for example, that have been associated with problematic claims from
that provider in the past.
Insurance Fraud Manager also flags fewer legitimate claims because it scores accurately even when
information is incomplete. Frequently it happens, for example, that a claim for an ambulance trip to
a hospital will be received before the bill for medical care provided by the hospital. Insurance Fraud
Manager can still look at the claim in the context of other data (e.g., previous involvement in fraud
or abuse by the provider, the kinds of procedures the patient has had in the past, what the expected
lag time is for incoming claims on the types of treatments the patient is likely to undergo), thereby
differentiating between high-risk and low-risk claims.
Portfolio
Size−Accounts
Characteristics
of Prepayment
Scoring
• Comprehensive data (historical and new)
2,000,000
Average
$2,000
• Analysis at the
claimBalance
level
• End users arePortfolio
typically claims adjustors with some activity by fraud investigators and nurse
Value
$4,000,000,000
case managers
Portfolio Size−Active Accounts
Advantages
Portfolio
Value−Active
• Avoids
“pay and
chase” Accounts
1,600,000
$3,200,000,000
• Prevents significant losses from occurring by detecting problems at earliest possible moment
24 Month Recovery Rate
12%
• Helps payer organizations influence and shape provider behavior
Fast-cycle Checkpoints
Without having to wait for postpayment analysis (often invoked semi-annually or at year-end), health
care payers can focus in very quickly on those providers with the most problems.
Insurance Fraud Manager statistically aggregates a year’s worth of scores for each provider.
It then generates a ranked list of providers based on the extent of aberration in their claims.
Insurance Fraud Manager generally repeats this score aggregation on a weekly basis, in which
case the “window” of a year’s worth of scores rolls forward each time by seven days. This approach
generates a view of provider riskiness that is at once fresh (since it captures very recent claims) and
thorough (since it reaches back to assess activity over time).
As a result, fast-cycle checkpoints act as an early warning system. They identify providers, for example,
with an unusual number of high-dollar claims, repetitive claims or claims missing data. By picking up
the first sign of problems, fast-cycle checkpoints enable payers to take action before losses mount.
Such capabilities will become increasingly important as more states begin enforcing time restrictions,
such as preventing insurers from going back longer than a year to recover overpayments.
© 2011 Fair Isaac Corporation. All rights reserved.
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FICO™ Insurance Fraud Manager
THE Health care CLAIMS PAYMENT SPECTRUM
Incoming
Claims
$
Postpayment
Prepayment
Seconds, minutes, hours
Days, Weeks
Fast-cycle checkpoints
Early warnings; stop fraud and abuse before losses mount
Portfolio
Size−Accounts
Characteristics
of Fast-Cycle
Checkpoints
• Uses comprehensive data (historical and new)
2,000,000
$2,000
Average Balance
• Statistically aggregates
scores at the entity level (by provider, member,
procedure codes)
• End users arePortfolio
typically fraud investigators and nurse care managers (for quality of care issues)
Value
$4,000,000,000
Advantages
Portfolio Size−Active Accounts
1,600,000
• Prevents significant losses from occurring as a result of delayed response to risk
Portfolio
Value−Active
Accounts
• Detects
known
and unknown
fraud schemes
24 Month Recovery Rate
$3,200,000,000
12%
Postpayment Analysis
Most of the methods employed by health care payers to review paid claims for fraud and abuse
are very labor intensive. FICO™ Insurance Fraud Manager, when used for postpayment analysis,
significantly increases detection accuracy while reducing analysis time and cost. It also increases
investigator productivity and recovery success.
THE Health care CLAIMS PAYMENT SPECTRUM
Incoming
Claims
$
Prepayment
Seconds, minutes, hours
Postpayment
Days, Weeks, Months
Large-scale postpayment analysis
Identify sources of largest losses; see systemic problems with policies and processes
© 2011 Fair Isaac Corporation. All rights reserved.
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FICO™ Insurance Fraud Manager
Postpayment analysis enables health care organizations to look at large quantities of claims, thereby
detecting statistical patterns not evident in smaller datasets. It is also a very effective means of
revealing systemic problems, chronic abusers and professional fraud schemes that unfold over time.
FICO™ Insurance Fraud Manager accurately detects such problems because it uses neural network
models to simultaneously analyze data from many different sources in complex, nonlinear ways that
surpass the capacity of the human brain. Neural networks detect subtle and sophisticated fraud and
abuse schemes invisible to systems that perform linear analysis or operate by rules alone.
The sophistication of this analysis can’t be overemphasized. Seemingly simple questions such as
“which procedures, when delivered together to the same patient, are indicative of fraud/abuse risk?”
are actually very complex. Every patient is different, and factors such as severity of illness, where
patients live relative to their provider(s), the services they’ve received in the past and their ages and
gender must be considered by the model in assessing the validity of the services billed. In addition,
thousands of factors within the health care system, such as differences between urban and rural
health care delivery and hyper-specialization within provider groups, must also be taken into account.
The neural networks are able to perform these advanced analyses efficiently because they operate
in conjunction with compact, efficient information packages called “profiles.” Profiles can condense
as much as a terabyte of data into about a thousand very potent and meaningful characteristics,
which are instantly accessed by Insurance Fraud Manager, eliminating the need for database queries.
Insurance Fraud Manager maintains profiles on a wide range of entities, including patients and
providers of every subspecialty. It also has profiles on combinations of entities.
A significant advantage of combining models and profiles with rules is that Insurance Fraud Manager
is both more precise and more flexible than solutions employing rules alone. Precision is increased
by the fact that the profiles are completely objective, having been created from the data itself. At
the same time, the richness of the data they represent, when analyzed by the sophisticated models,
enables very flexible decisioning—that is, decisions that take into account a complex picture of
interrelated factors and are highly nuanced. Rules, in contrast, are always to some extent subjective,
since they’ve been written by people who must draw conclusions from data and observation.
In execution, however, because of their “if, then” nature, rules tend to be quite inflexible, forcing
decisions in one direction or the other while ignoring subtleties that may be important.
In addition, Insurance Fraud Manager is able to spot potentially risky behaviors never seen before,
including emerging and changing fraud and abuse activity. This forward-looking detection is
important, since fraudsters are endlessly inventive and adept at changing their methods of operation.
Rule-based systems, in contrast, can detect only known fraud, since you can’t write a rule for
something you don’t know about.
© 2011 Fair Isaac Corporation. All rights reserved.
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FICO™ Insurance Fraud Manager
Characteristics of Postpayment Analysis
• Comprehensive data (historical and new)
• Stable data (almost all the claims coming in are for bills that have been received)
• Analysis at the aggregate level (by provider and member)
• Data-driven peer comparisons
• Provider summary reports
• End users are typically fraud investigators
Advantages
• Detects known and unknown fraud schemes
• Enables effective legal action in large, definitive cases
Full Spectrum Payment Optimization
With FICO™ Insurance Fraud Manager, prepayment scoring, fast-cycle checkpoints and postpayment
analysis are complementary and can be employed in ways that increase their mutual effectiveness.
For example, as each incoming claim is scored prior to payment, new data automatically updates the
historical data objects used by Insurance Fraud Manager’s analytics. Much of this historical data can
be used later to improve the precision of postpayment analytics.
Claims Investigation: Investigate Efficiently and Increase Recovery Amounts
Rules can be created within Insurance Fraud Manager to automatically route claims that score above
a specified threshold to a claims expert for review and dispatch. In prepayment scoring, the claim
may go first to a skilled adjuster; in fast-cycle checkpoints and prepayment analysis the destination
may be SIU or a clinical reviewer. In any case, the expert can use the case management functionality
to quickly understand and act on the problem.
The complete case management functionality provides immediate access to a wide array of summary
reports on entities (patients, providers, procedures, etc.) as well as to detailed claims data. Users work
efficiently because they can navigate instantly from entity-level information to claims-level data, and
vice versa.
Investigators could review a list of risky providers ranked by their scores. By clicking on a particular
provider, they could see the procedures that show up most frequently on high-scoring claims from
that provider. They could also see the types of problems being found, such as an unusual number of
high-dollar claims, repetitive claims or claims with missing data. They could then look at a list of all
relevant claims, quickly drilling down to detailed information on any or all of them.
Insurance Fraud Manager provides simple search, query and sort features, as well as standard
and customized reporting (open cases, investigations resulting in cost savings, etc.). Additionally,
investigators manage their workload, case documentation and recovery details from one central
location. To help resolve cases quickly and efficiently, investigators can generate correspondence
letters automatically, with the ability to attach supporting documents. And when it’s time to bring in
the legal department or government agencies, the case is already assembled and ready for hand-off.
© 2011 Fair Isaac Corporation. All rights reserved.
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FICO™ Insurance Fraud Manager
Flexible, Efficient Integration
FICO™ Insurance Fraud Manager interoperates with existing payer systems without the need for
costly, time-consuming application integration. Simple data feeds are all that is required to transmit
claims to Insurance Fraud Manager, and return decisions back to claims processing systems. Payer
organizations can therefore add state-of-the-art error, fraud and abuse detection to their processes
with minimum disruption and expense.
»» Summary:
Top 3 Reasons to
Deploy Insurance
Fraud Manager
1. Provides more opportunities for health care payers to avoid losses. Insurance Fraud Manager
is the only system that provides comprehensive protection. Because it detects error, fraud
and abuse at three points in the claims payment spectrum—prepayment scoring, fast-cycle
checkpoints and postpayment analysis—it minimizes losses along with the need for costly
recovery attempts.
2. Detects more error, fraud and abuse, with greater accuracy, than any other method. Insurance
Fraud Manager analyzes far more than just the information on the claim. It looks at the claim in
the context of vast amounts of other data and complex data interrelationships, thereby detecting
even subtle, hidden and emerging fraud schemes.
3. Focuses investigators on high-risk claims and the specific problems that make them risky.
Insurance Fraud Manager takes care of all the complexities of claims analysis in the background,
delivering results in the action-ready form of ranked scores with explanations. Human experts
can easily review and act upon the evidence presented to them, then make claims decisions very
efficiently.
»» Insurance Fraud Manager
Implementation
How It Works
The graphic below shows the process flow Insurance Fraud Manager uses to score claims and entities.
A step-by-step “walk-through” explanation of the process follows.
Payer
Patient
Claim
Provider
Payment
Payer
Validation Rules
Eligibility, formation,
edits, warnings
Checkout
or EFT
Claim
Prepay
SIU
Payer adjudication process
(payment amount)
Prepayment
claims scoring
Claims
database
Fast-cycle
postpayment
analysis
Upgraded
Rules
Postpay
SIU
Large-scale
postpayment
analysis
© 2011 Fair Isaac Corporation. All rights reserved.
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FICO™ Insurance Fraud Manager
Process walk-through
• Following, prior to, or during claims adjudication, the claims data is transmitted to FICO™
Insurance Fraud Manager. Transmissions can occur in real time or in batches. Batches can be
sent with any frequency. Usually it’s done hourly or nightly for prepayment scoring and at
longer intervals (quarterly, semi-annually, annually) for postpayment analysis.
• The scoring engine invokes the appropriate predictive models to analyze the claims. It
attaches a risk score—indicating the level of aberration compared to usual treatment or
billing behavior—to the claim. On a scale of 0 to 1000, for example, a score of 200 would
indicate a lower risk than one of 800.
• Insurance Fraud Manager transmits the scores; payers can set thresholds, using rules to
automatically route all claims scoring above a certain number to the appropriate pending
queue for review.
• Complete case management enables claims adjusters, clinical analysts and SIU groups to
review ranked lists of high-scoring claims or entities, navigate quickly between summary
information and claims-level detail, perform powerful searches, queries and sorts, and
generate automated correspondence and reports. .
• Typically, when Insurance Fraud Manager is used for prepayment scoring, claims with
high numbers will be routed to a skilled claims adjuster as the first line of defense. These
adjusters will usually be able to dispatch pending claims quickly, the explanations and case
information provided by Insurance Fraud Manager immediately illuminating simple
problems that slipped through the adjudication process. Claims that still look risky can be
forwarded on to the SIU or, where clinical review is needed, to a nurse or other appropriately
trained professional.
• When Insurance Fraud Manager is used for postpayment analysis, high-scoring claims are
usually routed immediately to SIU. Again, however, rules can be set up to route some cases
to clinical review first, enabling some cases to be dispatched without SIU involvement and
others to arrive in the investigative queue with additional information inserted into the case
report by clinical reviewers.
• Reviewers make dispatch decisions. The case management functionality supports the process
of opening and conducting investigations.
»» Insurance Fraud Manager
Comes with IndustrySpecific Models
For Medical Payers
The complexity of the health care environment today makes it challenging for honest medical
providers to bill accurately. At the same time, unscrupulous providers find many ways to exploit these
complexities for their own gain.
Insurance Fraud Manager can help medical payers stop the abuse, improve claims handling policies
and influence billing behavior on a broad scale. It is available for medical payers in two separate and
compatible versions: one model for prepayment scoring and the other with multiple models for
postpayment analysis. The prepayment solution also includes fast-cycle checkpoint capabilities.
Both versions of Insurance Fraud Manager for medical payers maintain profiles on a wide range of
entities, including patients and providers. The prepayment version of Insurance Fraud Manager uses
these profiles as inputs to predictive models that detect aberrant billing and treatment behavior, such
as procedures being performed in extraordinary numbers or in an otherwise unusual manner. In one
© 2011 Fair Isaac Corporation. All rights reserved.
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FICO™ Insurance Fraud Manager
case, FICO™ Insurance Fraud Manager detected aberrant billing behavior (weekly 60-minute sessions
for treatment of encephalopathy), which led to the investigation of a psychiatrist billing as a general
practitioner in attempt to avoid cost containment oversight.
It catches inconsistent and illogical claims. A bill from a cardiac specialist, for example, may seem fine,
except that the patient isn’t seeing any other doctors. A claim for insulin appears legitimate, except
that the patient has no history of receiving medical care for diabetes.
Insurance Fraud Manager postpayment models use many of the same profiles. Profiles are used,
for example, to detect unusual behavior for an entity’s peer group. For example, Insurance Fraud
Manager detected a psychologist who was billing under the correct category, but for 112 patients
in an eight-hour day—a schedule that would have allowed him only 4.29 minutes per patient for a
treatment on which other providers in his peer group were spending 75-80 minutes.
The postpayment solution includes models that look at transportation services in relation to medical
services over time. It also includes models that recognize unusual behavior for a wide range of
“facilities.” These facilities model components can detect aberrations in duration of patient stay in a
facility (DRG upcoding) or overuse of resource utilization groups (RUGs) in Skilled Nursing Facilities.
These components can also be combined in ways that capture correlations and inconsistencies
across facilities. Examples might include inconsistencies between inpatient and outpatient activity
or suspicious movements of patients from one facility to another, such as a patient moving from a
hospice for the terminally ill to a hospital. On a larger scale, combined facilities models could detect
a suspicious volume of patient sharing among facilities, which might be indicative of large-scale,
organized billing fraud.
For Pharmacy Payers and Pharmacy Benefit Managers (PBMs)
Prescription drug coverage is one of the fastest growing components of government health care
budgets and commercial benefit coverage. Insurance Fraud Manager prepayment scoring helps
control these costs by enabling payers to detect and correct billing errors at the point of sale.
Fast-cycle checkpoints help auditors detect and stop significant fraud and abuse before losses mount.
Insurance Fraud Manager for pharmacy payers and PBMs maintains profiles on a wide range of
entities, including patients, pharmacies and prescribers, as well as profiles on combinations of these
entities. Powerful predictive models use these profiles as inputs, scoring each claim for its level of
aberration against usual behavior for that entity’s peer group. For example, pharmacies of a certain
size serving certain demographics will be compared to similar businesses; patients receiving certain
types of medications to other patients receiving the same drugs.
Based on this type of broad peer group analysis, Insurance Fraud Manager can detect problems
with claims, such as an illegitimate line item, for a drug never dispensed, hiding inside an otherwise
legitimate claim. These aberrations can lead auditors to pharmacies that are misrepresenting the
services they provide and/or diverting products for resale.
For Dental Payers
Insurance Fraud Manager performs provider analysis for dental payers. This version maintains profiles
on a wide range of entities, including patients and providers in the various dental subspecialties, such
as general dentistry, pediatric dentistry, orthodontia, oral surgery, etc. Using these profiles as inputs,
dental models are able to analyze the behavior of all key entities and entity combinations: providers,
© 2011 Fair Isaac Corporation. All rights reserved.
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FICO™ Insurance Fraud Manager
patients, provider-provider relationships, provider-patient relationships, etc. They are also able to
distinguish between the practice patterns of subspecialties and incorporate factors such as urban or
rural location and patient demographic base into
their analysis.
Dental claims are scored for their level of aberration in treatment and billing compared to usual
behavior in a peer group. In one case, FICO™ Insurance Fraud Manager detected a provider billing
for a high number of pulp vitality tests and limited oral evaluations compared to his peers during
a similar time period. This resulted in program changes that saved $8-9 million a year. (The payer
went from reimbursing $8 per tooth per visit, times 32 teeth, to $1 per visit.) The models will also
notice inconsistencies across multiple treatments on the same patient. For example, Insurance Fraud
Manager detected a bill for a crown on the same tooth on which a four-surface resin restoration had
reportedly been performed just three weeks before.
»» Conclusion
Analytics and Decisioning Reduce Health care Costs
One of the best ways to reduce health care costs without compromising quality of care is to reduce
the amount of funds paid out unnecessarily. Insurance Fraud Manager customers are achieving
ROI of 3:1 to 10:1. One early adopter, Government Employees Hospital Association (GEHA), a
450,000-member federal employee health plan, realized hard-dollar savings from one in eight
providers, enabling it to achieve 3:1 ROI within the first year.
Whether Insurance Fraud Manager is used prior to or after payment, it will reveal opportunities
to save and recoup money. Whether it is used solely to guide human decision makers or, where
appropriate, to drive some level of automated decision making, it will improve the amount of data
that can be considered in a very short time, the volume of claims that can be processed with deep
analysis and the productivity of all personnel involved in claims review.
Still, Insurance Fraud Manager is but one example of how advanced analytics can help health care
organizations make better decisions that improve the efficiency of operations and deliver lower costs
and improved care. Powerful analytics methods can be valuable in any area where complex decisions
must be made with a high degree of accuracy and consistency. For health care payers, opportunities
include underwriting, regulatory compliance, marketing, management of agents and other sales
channels and scheduling.
Today, payers in both the commercial and government sectors are beginning to recognize these
opportunities and explore the benefits. Soon analytics will become an integral part of the health care
landscape. Organizations that begin today will be in a position to lead.
For more information, go to www.fico.com
© 2011 Fair Isaac Corporation. All rights reserved.
page 12
FICO™ Insurance Fraud Manager
about FICO
FICO (NYSE:FICO) delivers superior predictive analytics solutions that drive smarter decisions. The
company’s groundbreaking use of mathematics to predict consumer behavior has transformed
entire industries and revolutionized the way risk is managed and products are marketed. FICO’s
innovative solutions include the FICO® Score—the standard measure of consumer credit risk in the
United States—along with industry-leading solutions for managing credit accounts, identifying and
minimizing the impact of fraud, and customizing consumer offers with pinpoint accuracy. Most of the
world’s top banks, as well as leading insurers, retailers, pharmaceutical companies and government
agencies, rely on FICO solutions to accelerate growth, control risk, boost profits and meet regulatory
and competitive demands. FICO also helps millions of individuals manage their personal credit health
through www.myFICO.com. Learn more at www.fico.com. FICO: Make every decision count™.
For more information US toll-free
+1 888 342 6336
International
+44 (0) 207 940 8718
email
info@fico.com
web
www.fico.com
FICO and “Make every decision count” are trademarks or registered trademarks of Fair Isaac Corporation in the United States and in other countries. Other product and company names herein may be trademarks of their
respective owners. © 2005-2011 Fair Isaac Corporation. All rights reserved.
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