Healthcare fraud, abuse and waste costs the industry nearly $80 billion per year. Predictive analytics and data mining techniques can help payers more closely monitor for fraudulent billing activities. These techniques analyze relationships within large amounts of data to predict and detect fraudulent claims, saving millions of dollars traditionally lost to healthcare fraud.

1. Detecting Health Insurance Fraud, Abuse and Waste using
Analytics
“Healthcare fraud is defined as the intentional misinterpretation of factual information on
a health plan’s claim to gain the untitled benefits”. Healthcare Abuse is defined as charging
the plan for services, not medically required, below the defined standards or unfairly
priced. “The Health insurance fraud is described as an intentional act of deceiving,
concealing, or misrepresenting information that results in health care benefits being paid to
an individual or group” – Wikipedia. Waste occurs when providers intentionally or
unintentionally misuse resources. For e.g. clinician prescribing expensive medications when
less expensive generic options are available, and in extreme cases, even performing
unnecessary procedures or surgeries.
Any Healthcare organization that exchanges money with service providers, customers and
vendors are prone to health insurance fraud and abuse. Health plans around the world are
losing more money than the amount of the Medical Loss Ratio (MLR). Examples of fraud
include: billing for services not rendered, misrepresenting the diagnosis to fraudulently
collect payment, soliciting, offering, or receiving a kickback, unbundling or "exploding"
charges and the never ending list goes on and on forever.
The real difference between fraud and abuse is the person's intent. Both acts have the same impact: they detract valuable
resources from the Health Plans that would otherwise be used to offer economical plans and provide efficient services to the
subscribers and higher reimbursement to the providers.
The National Health Care Anti-Fraud Association (NHCAA) estimated that 3% of all health care spending—or $68 billion—is lost to
health care fraud. While another estimate by the US government and law enforcement agencies reveal that the loss due to
healthcare fraud is a staggering $226 billion dollars.
Some of the most common types of fraud and abuse in Healthcare fall into one of two categories: Providers and Members.
Providers
• Misrepresentation of services with misleading Current Procedural Terminology (CPT) codes
• Falsifying claim information for higher payments
• Falsification of information in medical record documents, such as International Classification of Diseases, Ninth Revision,
Clinical Modification (ICD-9/10-CM) codes and treatment histories
• Charging for services not provided, unnecessary services, tests, procedures etc. provided to a non-qualifying case
• Duplicate submission of a claim for the same service
According to the National
Health Care Antifraud
Association of US, health care
fraud, waste and abuse strip
nearly $80 billion from the
health care industry each
year. The industry is facing an
urgent need to adopt more
proactive and advanced
analytic techniques in order to
protect benefit premiums and
to avoid passing along these
potentially avoidable costs to
consumers.

2. • "Upcoding" - charging for a more complex or expensive service than was actually provided
• Charging for services covered under the plan, while the service provided was different than charged service
• Members Using a member ID card that does not belong to that person
• Adding someone to a policy that is not eligible for coverage (i.e.,
grandchildren)
• Failing to remove someone from a policy when that person is no
longer eligible (i.e., a former spouse)
• "Doctor shopping" - visiting several doctors to obtain multiple
prescriptions
Though Payers have upgraded IT system to detect fraudulent claims based on processing rules; there is a flaw in rule based
detections. This flaw exposes the Payer to the unknown fraud, as these rule based systems focus on detecting the known
types of fraudulent cases. Therefore, rule based fraud systems are not a reliable method of detecting fraudulent claims.
Payers need to evolve out of the traditional way of single claim views into a more mature ability to analyze all possible
activities and relationship between the stakeholders including providers, members and suppliers. Payer organizations
need more than traditional rule based engines to detect the next generation of fraudulent activities. The answer to this
problem lies in the DATA. The data needs to be exploited using Analytics and Data Mining.
Payers need to go beyond conventional
methods of detecting new generation
unconventional frauds.
Predictive analytics using business
intelligence & data mining is the key to the
problem of detecting and preventing these
intelligent frauds and abuse claims.

3. Use of Data Mining, Statistics and Predictive Modeling
Predictive Analytics and Data Mining are perhaps the most effective and valuable tools for detecting healthcare fraud
and abuse.
Data Mining: Data Mining is an analytical process designed to explore data (usually large amounts of data - typically
business or market related) to search for consistent patterns and/or systematic relationships between variables, and
then to validate the findings by applying the detected patterns to new subsets of data. The most important objective of
predictive data mining is the predicting an outcome.
A critical step involved in fraud detection is to recognize the factors associated with fraud. What are the characteristics behaviors
seen before, during and after the fraud? What is the Phenomenon usually associated with fraud. Once these factors, characteristics
and behaviors are identified, data mining techniques can predict a potential fraud. Using sophisticated data mining techniques such
as Neural Network/Pattern Recognition, Memory Based Reasoning, Cluster Technique, Link Analysis, Decision Tree, Rule
Induction, and Principle Component Analysis etc. can be used to reduce fraudulent claims, saving millions of dollars.
A brief description of some of the techniques commonly used in fraud and abuse detection is as follows
Neural Network (NN): NN was designed to mimic the learning and analysis capabilities of the brain. It works in a black box fashion.
Neural networks are comprised of a series of interconnected nodes designed to map a set of inputs into one or more output signals.
A set of data is created and the neural network learns and analyzes the patterns based on previous known outcome. For an
example, a Health Plan with more than 30,000 records has 300 known records that are fraudulent claims. The data set updates the
neural network to learn and identify the difference between a fraudulent record and legitimate record. The network uses the
cognitive capability and then scans millions of claims to identify fraudulent records.
Memory Based Reasoning (MBR): Memory Based Reasoning is identifying similar cases from experience and applying the
information from these cases to the problem at hand. MBR finds neighbors similar to a new record and uses the neighbors for
classification and prediction. MBR cares about the existence of two operations.
Distance function: Assigns a distance between any two records and Combination functions; combines the results from the
neighbors to arrive at an answer.
Cluster Technique: Cluster analysis or clustering is the classification of data objects
into similarity groups called clusters according to a defined distance measure.
Clustering is a method of unsupervised learning and a common technique for
statistical data analysis and is used in many fields, including machine learning, data
mining, image analysis and bioinformatics. Popular clustering techniques include k-
means clustering and expectation maximization (EM) clustering.
Link Analysis (LA): LA is another technique for associating like records. As the name
implies the technique tries to find links between given data sets. LA finds association
and relationship among many objects which may not be otherwise apparent.
Decision Tree: A decision tree is a natural structure of knowledge. It is one of the
most popular classification algorithms in data mining. In this technique, there are
tree-shaped structures that represent sets of decisions. These decisions generate
Data mining consists of 3 stages
Stage 1: Initial exploration
(cleansing, data transformation,
Selecting sub-set of records etc),
Stage 2: Model building or
pattern identification with
validation/verification, and
Stage 3: Deployment (i.e., the
application of the model to new
data in order to generate
predictions)

4. rules for the classification of a dataset. Specific decision tree methods include Classification and Regression Trees (CART) and Chi
Square Automatic Interaction Detection (CHAID).
Rule Induction: It is one of the most important techniques of machine learning. It is a staging method of discovery by inducing rules
about a data set. The test creates values for the data sets to evaluate which other data are its strongest associated factors. It works
in a top-down approach, seeking at each stage an attribute to split on, that separates the classes best, and then recursively
processing the partitions resulted from the split.
Principle Component Analysis (PCA): PCA can be described as a data reduction approach to reduce multi- dimensional data sets to 2
or 3 dimensions for visual variance analysis.
How predictive data analytics helps in combating insurance frauds
The predictive analytics encompasses both the advanced analytics techniques, like statistics, data mining, text mining, and decision
support engines, like rule engine, score engine and optimization engines etc. The predictive models can analyze very complex and
interrelated relationship among thousands of data sets and produce a single number, usually called a score. The score predicts the
likelihood of occurrence of a certain behavior. Higher score means higher chances of occurrence of an event.
For example, a predictive model built to analyze possible provider fraudulent claims can yield a score for each of the variables
indicating potential providers submitting fraudulent claims. The variable score can be produced based on certain defined
parameters, such as:
• Providers who are treating more than 60-70 patients/day while the national or the state average is 30 patients per day
• Providers requesting laboratory and radiology tests at a rate twice the national or the state average for the similar condition
• Providers consulting more patients, with higher residential distance to the practice
• Providers prescribing certain drugs for example narcotics (Oxycontin) at a higher rate than the national and state average
The predictive model can produce a score for each of these parameters. Each score can have a justification for the score-helping
user to understand the reasons for the score. This supports the user’s action to route the claim to an appropriate authority for
further investigation.
How Banking has addressed fraudulent activities
Consumers, retail merchants, and the banking industry have been under fraudulent attacks for years. The credit card companies,
banks, and the government have partnered to implement regulatory changes, technology, and verifications to detect and prevent
credit card fraud. While it is impossible to stop fraud, the industry has prevented the loss of billions of dollars.

5. Conclusion
In fraudulent claims, usually a small number of claims contribute to the majority of the dollar amount. These high value claims can
be legitimate or illegitimate claims. The trick is to differentiate these legitimate claims from the fraudulent claims that require
special investigation. The claim departments are usually the first line of defense in any organization. But most of the fraudulent
transactions go undetected as the manual screening and identification is very tedious and time consuming. Payor organizations
need to evaluate strategies and use a combination of approaches that include Data Mining, Predictive Analytics, and human review
instead of single rule based engine to combat frauds. Apart from technology, the individual members and providers need to be
more vigilant.
By adopting predictive analytics and data mining techniques, the healthcare ecosystem can more closely monitor, detect, and
prevent fraudulent billing activities at the provider, payor, and member level. By investing in these new techniques, the ecosystem
can become more transparent, and spend fewer dollars paying for fraudulent claims, and shift resources to more value added
services in the healthcare delivery chain.
1. http://www.nhcaa.org/eweb/DynamicPage.aspx?webcode=anti_fraud_resource_centr&wpscode=TheProblemOfHCFraud
2. Richardson, Robert J. "Monitoring Sale Transactions for Illegal Activity". Monitoring Sale Transactions for Illegal Activity.
Retrieved 14 July 2011
About Author:
Dr. Nitin Verma, Senior Vice President & Global Head for Healthcare Practice at Evosys, has over 20 years of experience in redefining
healthcare delivery through IT. He is a healthcare technology evangelist with a vision to '‘Healthcare Simplified’. An international
speaker on Enterprise Healthcare Analytics, Chronic Disease Management & Health Insurance Best Practices has authored several
white papers. He is a qualified clinician with MS (Pharmaceutical Sciences) & an MBA in Healthcare Management. He spearheaded
several large healthcare initiatives in various states of India including prestigious sickle cell screening program with the Government
of Gujarat. He is one of the key architects involved in design, development and deployment of many low cost healthcare solutions
for the Health Management & Research Institute (HMRI) enabling healthcare delivery to 42 million underserved rural populations in
Andhra Pradesh (India).

6. Conclusion
In fraudulent claims, usually a small number of claims contribute to the majority of the dollar amount. These high value claims can
be legitimate or illegitimate claims. The trick is to differentiate these legitimate claims from the fraudulent claims that require
special investigation. The claim departments are usually the first line of defense in any organization. But most of the fraudulent
transactions go undetected as the manual screening and identification is very tedious and time consuming. Payor organizations
need to evaluate strategies and use a combination of approaches that include Data Mining, Predictive Analytics, and human review
instead of single rule based engine to combat frauds. Apart from technology, the individual members and providers need to be
more vigilant.
By adopting predictive analytics and data mining techniques, the healthcare ecosystem can more closely monitor, detect, and
prevent fraudulent billing activities at the provider, payor, and member level. By investing in these new techniques, the ecosystem
can become more transparent, and spend fewer dollars paying for fraudulent claims, and shift resources to more value added
services in the healthcare delivery chain.
1. http://www.nhcaa.org/eweb/DynamicPage.aspx?webcode=anti_fraud_resource_centr&wpscode=TheProblemOfHCFraud
2. Richardson, Robert J. "Monitoring Sale Transactions for Illegal Activity". Monitoring Sale Transactions for Illegal Activity.
Retrieved 14 July 2011
About Author:
Dr. Nitin Verma, Senior Vice President & Global Head for Healthcare Practice at Evosys, has over 20 years of experience in redefining
healthcare delivery through IT. He is a healthcare technology evangelist with a vision to '‘Healthcare Simplified’. An international
speaker on Enterprise Healthcare Analytics, Chronic Disease Management & Health Insurance Best Practices has authored several
white papers. He is a qualified clinician with MS (Pharmaceutical Sciences) & an MBA in Healthcare Management. He spearheaded
several large healthcare initiatives in various states of India including prestigious sickle cell screening program with the Government
of Gujarat. He is one of the key architects involved in design, development and deployment of many low cost healthcare solutions
for the Health Management & Research Institute (HMRI) enabling healthcare delivery to 42 million underserved rural populations in
Andhra Pradesh (India).