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					What is Insurance fraud?



Insurance Fraud accounts for the largest volume of financial freak, second only to tax
evasion. It has existed ever since the beginning of insurance as a commercial enterprise.
Fraudulent claims account for a significant portion of all claims received by insurers, and
cost billions of dollars annually. Types of insurance fraud are very diverse, and occur in
all areas of insurance. Insurance crimes also range in severity, from slightly exaggerating
claims to deliberately causing accidents or damage. Fraudulent activities also affect the
lives of innocent people, both directly through accidental or purposeful injury or damage,
and indirectly as these crimes cause insurance premiums to be higher. Insurance fraud
poses a very significant problem, and governments and other organizations are making
efforts to deter such activities.

Insurance companies are susceptible to fraud because false insurance claims can be made
to appear like ordinary claims. This allows fraudsters to file claims for damages that
never occurred, and so obtain payment with little or no initial cost.

The most common form of insurance fraud is inflating of loss.

Losses due to insurance fraud
It is virtually impossible to determine an exact value for the amount of money stolen
through insurance fraud. Insurance fraud is designed to be undetectable, unlike visible
crimes such as robbery or murder. As such, the number of cases of insurance fraud that
are detected is much lower than the number of acts that are actually committed. The best
that can be done is to provide an estimate for the losses that insurers suffer due to
insurance fraud. The Coalition Against Insurance Fraud estimates that in 2006 a total of
about $80 billion was lost in the United States due to insurance fraud. According to
estimates by the Insurance Information Institute, insurance fraud accounts for 10%, or
about $30 billion, of losses in the property and casualty insurance industries in the United
States. The National Health Care Anti-Fraud Association estimates that 3% of the health
care industry‘s expenditures in the United States are due to fraudulent activities,
amounting to a cost of about $51 billion. Other estimates attribute as much as 10% of the
total healthcare spending in the United States to fraud—about $115 billion annually. In
the United Kingdom, the Insurance Fraud Bureau estimates that the loss due to insurance
fraud in the United Kingdom is about £1.5 billion ($3.08 billion), causing a 5% increase
in insurance premiums. The Insurance Bureau of Canada estimates that personal injury
fraud in Canada costs about C$500 million annually.

Hard vs. soft fraud
Insurance fraud can be classified as either hard fraud or soft fraud.

Hard fraud occurs when someone deliberately plans or invents a loss, such as a collision,
auto theft, or fire that is covered by their insurance policy in order to receive payment for
damages. Criminal rings are sometimes involved in hard fraud schemes that can steal
millions of dollars.

Soft fraud, which is far more common than hard fraud, is sometimes also referred to as
opportunistic fraud. This type of fraud consists of policyholders exaggerating otherwise
legitimate claims. For example, when involved in a collision an insured person might
claim more damage than was really done to his or her car. Soft fraud can also occur
when, while obtaining a new insurance policy, an individual misreports previous or
existing conditions in order to obtain a lower premium on their insurance policy.




Types of insurance fraud
Life insurance

An example of life insurance fraud is the John Darwin disappearance case, an ongoing
investigation into the faked death of British former teacher and prison officer John
Darwin, who turned up alive in December 2007, five years after he was thought to have
died in a canoeing accident. Darwin was reported as "missing" after failing to report to
work following a canoeing trip on March 21, 2002. He reappeared on December 1, 2007,
claiming to have no memory of the past five years.

Health care insurance

According to Roger Feldman, Blue Cross Professor of Health Insurance at the University
of Minnesota, one of the main reasons that medical fraud is such a prevalent practice is
that nearly all of the parties involved find it favorable in some way. Many physicians see
it as necessary to provide quality care for their patients. Many patients, although
disapproving of the idea of fraud, are sometimes more willing to accept it when it affects
their own medical care. Program administrators are often lenient on the issue of insurance
fraud, as they want to maximize the services of their providers.

The most common perpetrators of healthcare insurance fraud are health care providers.
One reason for this, according to David Hyman, a Professor at the University of
Maryland School of Law, is that the historically prevailing attitude in the medical
profession is one of ―fidelity to patients‖. This incentive can lead to fraudulent practices
such as billing insurers for treatments that are not covered by the patient‘s insurance
policy. To do this, physicians often bill for a different service, which is covered by the
policy, than that which was rendered.

Another motivation for insurance fraud in the healthcare industry, just as in all other
types of insurance fraud, is a desire for financial gain. Public healthcare programs such as
Medicare and Medicaid are especially conducive to fraudulent activities, as they are often
run on a fee-for-service structure. Physicians use several fraudulent techniques to achieve
this end. These can include ―up-coding‖ or ―upgrading,‖ which involve billing for more
expensive treatments than those actually provided; providing and subsequently billing for
treatments that are not medically necessary; scheduling extra visits for patients; referring
patients to another physician when no further treatment is actually necessary; "phantom
billing," or billing for services not rendered; and ―ganging,‖ or billing for services to
family members or other individuals who are accompanying the patient but who did not
personally receive any services.

Automobile insurance

The Insurance Research Council estimated that in 1996, 21 to 36 percent of auto-
insurance claims contained elements of suspected fraud. There is a wide variety of
schemes used to defraud automobile insurance providers. These ploys can differ greatly
in complexity and severity. Richard A. Derrig, vice president of research for the
Insurance Fraud Bureau of Massachusetts, lists several ways that auto-insurance fraud
can occur. Examples of soft auto-insurance fraud can include filing more than one claim
for a single injury, filing claims for injuries not related to an automobile accident,
misreporting wage losses due to injuries, or reporting higher costs for car repairs than
those that were actually paid. Hard auto-insurance fraud can include activities such as
staging automobile collisions, filing claims when the claimant was not actually involved
in the accident, submitting claims for medical treatments that were not received, or
inventing injuries.. Another example is that a person may illegally register their car to a
location that would net them cheaper insurance rates than where they actually live,
sometimes called "rate evasion". For example, some drivers in Brooklyn drive with
Pennsylvania license plates because registering their car in a rural part of Pennsylvania
will cost a lot less than registering it in Brooklyn. Another form of automobile insurance
fraud, known as "fronting," involves registering someone other than the real primary
driver of a car as the primary driver of the car. For example, parents might list themselves
as the primary driver of their children's vehicles to avoid young driver premiums. Hard
fraud can also occur when claimants falsely report their vehicle as stolen. Soft fraud
accounts for the majority of fraudulent auto-insurance claims.

"Crash for cash" scams may involve random unaware strangers, set to appear as the
perpetrators of the orchestrated crashes. Such techniques are the classic rear-end shunt
(the driver in front suddenly slams on the brakes, eventually with brake lights disabled),
the decoy rear-end shunt (when following one car, another one pulls in front of it,
causing it to break sharply, then the first car drives off) or the helpful wave shunt (the
driver is waved in to a line of queuing traffic by the scammer who promptly crashes, then
denies waving)

Organized crime rings can also be involved in auto-insurance fraud, sometimes carrying
out schemes that are very complex. An example of one such ploy is given by Ken
Dornstein, author of Accidentally, on Purpose: The Making of a Personal Injury
Underworld in America. In this scheme, known as a ―swoop-and-squat,‖ one or more
drivers in ―swoop‖ cars force an unsuspecting driver into position behind a ―squat‖ car.
This squat car, which is usually filled with several passengers, then slows abruptly,
forcing the driver of the chosen car to collide with the squat car. The passengers in the
squat car then file a claim with the other driver‘s insurance company. This claim often
includes bills for medical treatments that were not necessary or not received.

Property insurance

Possible motivations for this can include obtaining payment that is worth more than the
value of the property destroyed, or to destroy and subsequently receive payment for
goods that could not otherwise be sold. According to Alfred Manes, the majority of
property insurance crimes involve arson. One reason for this is that any evidence that a
fire was started by arson is often destroyed by the fire itself. According to the United
States Fire Administration, in the United States there were approximately 31,000 fires
caused by arson in 2006, resulting in losses of $755 million. Example: The Moulin Rouge
in Las Vegas was struck by arson twice within 6 years.

Council compensation claims

The fraud involving claims from the councils' insurers suppose staging damages blamable
on the local authorities (mostly falls and trips on council owned land) or inflating the
value of existing damages.

Detecting insurance fraud
The detection of insurance fraud generally occurs in two steps. The first step is to identify
suspicious claims that have a higher possibility of being fraudulent. This can be done by
computerized statistical analysis or by referrals from claims adjusters or insurance agents.
Additionally, the public can provide tips to insurance companies, law enforcement and
other organizations regarding suspected, observed, or admitted insurance fraud
perpetrated by other individuals. Regardless of the source, the next step is to refer these
claims to investigators for further analysis.

Due to the sheer number of claims submitted each day, it would be far too expensive for
insurance companies to have employees check each claim for symptoms of fraud.
Instead, many companies use computers and statistical analysis to identify suspicious
claims for further investigation. There are two main types of statistical analysis tools
used: supervised and unsupervised. In both cases, suspicious claims are identified by
comparing data about the claim to expected values. The main difference between the two
methods is how the expected values are derived.

In a supervised method, expected values are obtained by analyzing records of both
fraudulent and non-fraudulent claims. According to Richard J. Bolton and David B.
Hand, both of Imperial College in London, this method has some drawbacks as it requires
absolutely certainty that those claims analyzed are actually either fraudulent or non-
fraudulent, and because it can only be used to detect types of fraud that have been
committed and identified before.

Unsupervised methods of statistical detection, on the other hand, involve detecting claims
that are abnormal. Both claims adjusters and computers can also be trained to identify
―red flags,‖ or symptoms that in the past have often been associated with fraudulent
claims. Statistical detection does not prove that claims are fraudulent; it merely identifies
suspicious claims that need to be investigated further.

Fraudulent claims can be one of two types. They can be otherwise legitimate claims that
are exaggerated or ―built up,‖ or they can be false claims in which the damages claimed
never actually occurred. Once a built up claim is identified, insurance companies usually
try to negotiate the claim down to the appropriate amount. Suspicious claims can also be
submitted to ―special investigative units‖, or SIUs, for further investigation. These units
generally consist of experienced claims adjusters with special training in investigating
fraudulent claims. These investigators look for certain symptoms associated with
fraudulent claims, or otherwise look for evidence of falsification of some kind. This
evidence can then be used to deny payment of the claims or to prosecute fraudsters if the
violation is serious enough.

Legislation
National and local governments, especially in the last half of the twentieth century, have
recognized insurance fraud as a serious crime, and have made efforts to punish and
prevent this practice. Some major developments are listed below:

      for fraud as imprisonment up to ten years, a fine, or both.

Examples
Following are some examples of real instances of insurance fraud that occurred in recent
years:

      According to a report by a United States district court in Illinois, a psychiatrist
       who practiced as the Assistant Medical Director and Medical Director at a
       psychiatric facility in Illinois from 1998 through 2002 submitted claims to
       Medicare for psychiatric and psychotherapy services that he in fact never actually
       provided. He also ―up-coded,‖ or billed for more expensive services than those
       that were actually provided, many claims that he submitted to Medicare. In
       addition, he admitted patients that did not qualify for treatment so that he could
       submit bills for hospital care even though it was not medically necessary for those
       patients. Through these schemes, this psychiatrist was able to fraudulently obtain
       $875,881 in Medicare Reimbursements before his conviction in February 2005.[51]

      The Insurance Information Institute conducted a study on organized crime rings in
       New York City that have fraudulently exploited the personal injury protection
       policies of no-fault automobile insurance plans throughout the beginning of the
       21st century. This has often been achieved when a ―runner‖ is paid to organize an
       intentional collision, often including multiple passengers. These passengers then
       are taken to ―medical mills,‖ which are either real or nonexistent facilities that file
       claims for reimbursement for treatments that are unnecessary and often not
       received. This practice has caused the cost of claims in New York City to rise by
       32.1% in 2006, as opposed to only a 4.5% increase in 1998.[52]

      According to the Coalition Against Insurance Fraud, a former business executive
       from Chicago resorted to insurance fraud to pay off his debt of over $672,000. He
       set fire to his own home in order to collect insurance money on it. In order to
       disguise this act of arson, he trapped his ninety year old mother in the basement
       while the house was burning so that the fire would appear to be a suicide. He
       received about $600,000 in insurance money, but was eventually convicted on
       several charges and sentenced to 190 years in federal prison.[53]

How the detection of insurance fraud succeeds and fails
Insurance fraud is a serious and growing problem, and there is widespread recognition
that traditional approaches to tackling fraud are inadequate. Studies of insurance fraud
have typically focused upon identifying characteristics of fraudulent claims and
claimants, and this focus is apparent in the current wave of forensic and data-mining
technologies for fraud detection. An alternative approach is tounderstand and then
optimize existing practices in the detection of fraud. We report an ethnographicstudy that
explored the nature of motor insurance fraud-detection practices in two leading insurance
companies. The results of the study suggest that an occupational focus on the practices of
fraud detection can complement and enhance forensic and data-mining approaches to the
detection of potentially fraudulent claims.



CBI probes Rs 50 lakh insurance fraud

Express News Service

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Pune, August 14 The Central Bureau of Investigation, Mumbai has registered a case of
forgery and fraud against a PMC employee and two members of a local NGO who
allegedly misappropriated the insurance money of over 2,000 beneficiaries of LIC‘s
Janashree policy in Pune and nearby areas. According to CBI, over Rs 50 lakh of
insurance money has been misappropriated.

The CBI officials raided the residences, offices of the NGO and visited the LIC
headquarters as well as a Karad Urban Co-op Bank branch on Thursday in a bid to seize
‗incriminating documents‘. The CBI had learnt that the beneficiaries of the policy were
being cheated of the insurance money as the fraudsters opened bogus bank accounts and
misappropriated the money. ―We recently registered a case of forgery and fraud under
IPC and Prevention of Corruption Act against the three accused and unknown officials of
the LIC and Karad Bank,‖ said a CBI official on the condition of anonymity.

The Janashree policy is an insurance scheme which has people below the poverty line as
beneficiaries and NGOs as nodal agencies. A senior LIC officer confirmed that CBI
officials had visited the office. ―Two months back too the CBI had visited our office with
a similar case of a Solapur-based NGO misappropriating the insurance money. Later they
had ruled out LIC‘s involvement,‖ he said.

When contacted, the General Manager of the Raviwar Peth branch of Karad Bank, Suraj
Shah said he was unaware of any such CBI inquiry.




Fraud is a million dollar business and it is increasing every year. The PwC global
economic survey 2007 suggests that close to 50% of companies worldwide reported
fallen victim to fraud in the past two years.

Fraud involves one or more persons who intentionally act secretly to deprive another of
something of value, for their own benefit. Fraud is as old as humanity itself and can take
an unlimited variety of different forms. However, in recent years, the development of
new technologies has also provided further ways in which criminals may commit fraud
(Bolton and Hand 2002). In addition to that, business reengineering, reorganization or
downsizing may weaken or eliminate control, while new information systems may
present additional opportunities to commit fraud.
Detecting Fraud

Traditional ways of data analysis have been in use since long time as a method of
detecting fraud. They require complex and time-consuming investigations that deal with
different domains of knowledge like financial, economics, business practices and law.
Fraud often consists of many instances or incidents involving repeated transgressions
using the same method. Fraud instances can be similar in content and appearance but
usually are not identical (Palshikar 2002).

The first industries to use data analysis techniques to prevent fraud were the telephony
companies, the insurance companies and the banks (Decker 1998). One early example of
successful implementation of data analysis techniques in the banking industry is the
Falcon fraud assessment system, which is based on a neural network shell (Brachman et
al. 1996).

Retail industries also suffer from fraud at POS. Some supermarkets have started to make
use of digitized closed-circuit television (CCTV) together with POS data of most
susceptible transactions to fraud (Weir 2001).

Internet transactions have recently raised big concerns. Kerr (2002) shown that internet
transaction fraud is 12 times higher than in-store fraud.

Fraud that involves cell phones, insurance claims, tax return claims, credit card
transactions etc represent significant problems for governments and businesses, but yet
detecting and preventing fraud is not a simple task. Fraud is an adaptive crime, so it needs
special methods of intelligent data analysis to detect and prevent it. These methods exists
in the areas of Knowledge Discovery in Databases (KDD), Data Mining, Machine
Learning and Statistics. They offer applicable and successful solutions in different areas
of fraud crimes.

Techniques used for fraud detection fall into two primary classes: statistical techniques
and artificial intelligence (Palshikar 2002). Examples of statistical data analysis
techniques are:

      Data preprocessing techniques for detection, validation, error correction, and
       filling up of missing or incorrect data.
      Calculation of various statistical parameters such as averages, quantiles,
       performance metrics, probability distributions, and so on. For example, the
       averages may include average length of call, average number of calls per month
       and average delays in bill payment.
      Models and probability distributions of various business activities either in terms
       of various parameters or probability distributions.
      Computing user profiles.
      Time-series analysis of time-dependent data.
      Clustering and classification to find patterns and associations among groups of
       data.
      Matching algorithms to detect anomalies in the behavior of transactions or users
       as compared to previously known models and profiles. Techniques are also
       needed to eliminate false alarms, estimate risks, and predict future of current
       transactions or users.

Fraud management is a knowledge-intensive activity. The main AI techniques used for
fraud management include:

      Data mining to classify, cluster, and segment the data and automatically find
       associations and rules in the data that may signify interesting patterns, including
       those related to fraud.
      Expert systems to encode expertise for detecting fraud in the form of rules.
      Pattern recognition to detect approximate classes, clusters, or patterns of
       suspicious behavior either automatically (unsupervised) or to match given inputs.
      Machine learning techniques to automatically identify characteristics of fraud.
      Neural networks that can learn suspicious patterns from samples and used later to
       detect them.

Other techniques such as link analysis, Bayesian networks, decision theory, land
sequence matching are also used for fraud detection (Palshikar 2002).

Machine Learning and Data Mining

Early data analysis techniques were oriented toward extracting quantitative and statistical
data characteristics. These techniques facilitate useful data interpretations and can help to
get better insights into the processes behind the data. Although the traditional data
analysis techniques can indirectly lead us to knowledge, it is still created by human
analysts (Michalski et al. 1998).

To go beyond, a data analysis system has to be equipped with a substantial amount of
background knowledge, and be able to perform reasoning tasks involving that knowledge
and the data provided (Michalski et al. 1998). In effort to meet this goal, researchers have
turned to ideas from the machine learning field. This is a natural source of ideas, since the
machine learning task can be described as turning background knowledge and examples
(input) into knowledge (output).

If data mining results in discovering meaningful patterns, data turns into information.
Information or patterns that are novel, valid and potentially useful are not merely
information, but knowledge. One speaks of discovering knowledge, before hidden in the
huge amount of data, but now revealed.

Supervised and Unsupervised Learning

The machine learning and artificial intelligence solutions may be classified into two
categories: 'supervised' and 'unsupervised' learning. In supervised learning, samples of
both fraudulent and non-fraudulent records are used. This means that all the records
available are labelled as 'fraudulent' or 'non-fraudulent'. After building a model using
these training data, new cases can be classified as fraudulent or legal (Jans et al.).

Furthermore, this method is only able to detect frauds of a type, which has previously
occurred. In contrast, unsupervised methods don't make use of labelled records. These
methods seek for accounts, customers, suppliers, etc. that behave 'unusual' in order to
output suspicion scores, rules or visual anomalies, depending on the method (Bolton and
Hand 2002).

Whether supervised or unsupervised methods are used, note that the output gives us only
an indication of fraud likelihood. No stand alone statistical analysis can assure that a
particular object is a fraudulent one. It can only indicate that this object is more likely to
be fraudulent than other objects (Jans et al.).

Some Research Contributions

Supervised Methods

The field of neural networks has been extensively explored as a supervised method. Jans
et al. mention the studies of Barson, Field, Davey, McAskie, and Frank (Barson et al.)
and Green and Choi (1997) all use neural network technology for detecting respectively
fraud in mobile phone networks (Barson et al.) and financial statement fraud. Lin et al.
(2003) apply a fuzzy neural net, also in the domain of fraudulent financial reporting. Both
Brause et al. (1999) and Estevez et al. (2006) use a combination of neural nets and rules.

Bayesian learning neural network is implemented for credit card fraud detection by Maes
et al. (2002) for telecommunications fraud by Ezawa and Norton (1996) and for auto
claim fraud detection by Viaene et al. (2005). In the same field as Viaene et al. (2005),
insurance fraud, Major and Riedinger (2002) presented a tool for the detection of medical
insurance fraud. They proposed a hybrid knowledge/statistical-based system, where
expert knowledge is integrated with statistical power.

Another example of combining different techniques can be found in Fawcett and Provost
(1997). A series of data mining techniques for the purpose of detecting cellular clone
fraud is used. Specifically, a rule-learning program to uncover indicators of fraudulent
behaviour from a large database of customer transactions is implemented.

Fawcett and Provost (1999) the Activity Monitoring is introduced as a separate problem
class within data mining with a unique framework.

Stolfo et al. and Lee et al. delivered some interesting work on intrusion detection. They
provided a framework, MADAM ID, for Mining Audit Data for Automated models for
Intrusion Detection. Next to this, the results of the JAM project are discussed.

Cahill et al. (2000) design a fraud signature, based on data of fraudulent calls, to detect
telecommunications fraud. For scoring a call for fraud its probability under the account
signature is compared to its probability under a fraud signature. The fraud signature is
updated sequentially, enabling event-driven fraud detection.

Link analysis comprehends a different approach. It relates known fraudsters to other
individuals, using record linkage and social network methods (Wasserman and Faust
1998). Cortes et al. (2002) proposed a solution to fraud detection in this field (Phua,
2005).



Unsupervised Methods

Some important studies with unsupervised learning with respect to fraud detection should
be mentioned. For example, Bolton and Hand use Peer Group Analysis and Break Point
Analysis applied on spending bevaviour in credit card accounts. Peer Group Analysis
detects individual objects that begin to behave in a way different from objects to which
they had previously been similar. Another tool Bolton and Hand develop for behavioural
fraud detection is Break Point Analysis. Unlike Peer Group Analysis, Break Point
Analysis operates on the account level. A break point is an observation where anomalous
behaviour for a particular account is detected. Both the tools are applied on spending
behaviour in credit card accounts.

Also Murad and Pinkas (1999) focus on behavioural changes for the purpose of fraud
detection and present three-level-profiling. As the Break Point Analysis from Bolton and
Hand, the three-level-profiling method operates at the account level and it points any
significant deviation from an account's normal behaviour as a potential fraud. In order to
do this, 'normal' profiles are created based on data without fraudulent records (semi
supervised). To test the method, the three-level-profiling is applied in the area of
telecommunication fraud. In the same field, also Burge and Shawe-Taylor (2001) use
behaviour profiling for the purpose of fraud detection. However, using a recurrent neural
network for prototyping calling behaviour, unsupervised learning is applied. ] Cox et al.
(1997) combines human pattern recognition skills with automated data algorithms. In
their work, information is presented visually by domain-specific interfaces, combining
human pattern recognition skills with automated data algorythms (Jans et al.).

AHMEDABAD: The Motor Accident Claims Tribunal (MACT) has pulled up owners of
two tractors as well as the officer at Adalaj police station for trying to defraud the
National      Insurance     Company         (NIC)       through       false     claims.

MACT judge DT Soni ordered tractor owners KD Patel and GB Patel and cop TJ Patel to
pay Rs 25,000 as costs to NIC. He directed NIC regional manager to file a criminal
complaint                  against                  the                   culprits.

NIC has been told to pay Rs 2000 to its advocate MJ Parikh for uncovering this fraud.

The case involves a couple and their minor son who met with an accident while travelling
in an autorickshaw. Rash and negligent driving by a tractor driver led to the accident,
which injured applicant MN Darbar and claimed lives of his wife and son.

The tractor owned by KD Patel was not insured, yet he got it entered as insured tractor
and got the number changed in the FIR with the police officer's help. MJ Parikh, during
verification   at    the    police    station,    stumbled       upon     the     fraud.

The judge noted that wrongdoers filed fictitious and false claims by impleading the false
and wrong owners by adjustment of the insured vehicles instead of uninsured vehicles.

"On account of lack of ample and cogent evidence, the insurance companies' cases
alleging fraud are not established," stated the judge.




      WORLD TRADE CENTER FRAUD

       Since the tragic events of September 11, the Frauds Bureau has been fast-
       tracking World Trade Center claims to ensure that they receive prompt
       attention. A number of arrests have been made in WTC-related cases and the
       Bureau is investigating additional complaints. More arrests are anticipated.

       Ajay Chawla was convicted of insurance fraud, attempted aggravated theft by
       deception, telecommunications fraud and falsification on 8/16/02 in Common
       Pleas Court in Butler County, Ohio. The case is believed to be Ohio‘s only case
       of insurance fraud related to September 11. Chawla filed a claim against a
       $100,000 life insurance policy, maintaining that his father had been inside the
       World Trade Center when it collapsed. However, evidence revealed that
       Chawla knew his father was alive and well in his native India. He faces up to
       eight years in prison at sentencing on September 16, 2002. The coordinated
       efforts of the Frauds Bureaus and local police departments in New York, Ohio
       and Illinois led to his arrest. In addition to Chawla‘s, the New York Insurance
       Frauds Bureau has tracked at least 55 suspected fraudulent September 11-
       related claims.

      CROSSING THE HUDSON PART II
       Arrested on 8/30/02
       Charged with criminal possession of stolen property in the 3rd degree

       A Delaware County woman was arrested and charged with possession of a
       stolen 1995 Mark VIII. The car was reported stolen in New Jersey and prior to
       its recovery, Liberty Mutual Insurance Company paid the owner more than
       $13,000. During an investigation by the Frauds Bureau, the Delaware County
    Sheriff‘s Office and the New Jersey Office of the Insurance Frauds Prosecutor,
    evidence was uncovered that this defendant with others, including the owner of
    the car, had conspired in a fraud scheme that began in New Jersey and
    proceeded to New York. One of the other conspirators in this case was arrested
    on 7/23/02 in this ongoing investigation and more arrests are expected.

   KEY EVIDENCE
    Arrested on 8/27/02
    Charged with insurance fraud, attempted grand larceny, making a punishable
    false written statement and falsely reporting an incident

    An investigation by the Frauds Bureau and the Staten Island District Attorney‘s
    Office led to the arrest of a local man charged with falsely reporting his car
    stolen and filing a fraudulent insurance claim. However, the car was recovered
    during the investigation and upon examination, experts discovered that this
    type of car required a specific key in order to start the engine. The fact that
    there was no damage to the steering column or the transmission also bolstered
    the case.

   WHAT’S UP, DOC
    Arrested on 8/27/02
    Charged with insurance fraud in the 3rd and 4th degrees, grand larceny in the
    3rd degree and scheme to defraud in the 1st degree

    A Brooklyn physician was arrested on charges that between 9/97 and 5/00, he
    submitted no-fault claims for services that were never rendered. Moreover,
    services, when they were provided, were performed by unlicensed individuals.
    Two investigators – one each from the Frauds Bureau and the State Attorney
    General‘s Office – working undercover, visited the doctor‘s office posing as
    accident victims. They were asked to sign a register indicating they had
    received treatment on 44 days, none of which were they present at the doctor‘s
    office. However, the doctor submitted no-fault claims for each of these dates
    and as a result received more than $5,000 in fraudulent reimbursement.

   DO THE MATH
    Arrested on 8/27/02
    Charged with insurance fraud in the 3rd degree and grand larceny in the 3rd
    degree

    The defendant in this case was accused of submitting fraudulent receipts in
    support of her workers‘ compensation claim indicating that between 1/97 and
    7/00, she spent $6,640 for prescriptions she required as a result of a work-
    related injury. An investigation by the Frauds Bureau and the New York State
    Police revealed that she had altered her supporting documents to conceal the
    fact that she had paid only a $2 co-pay for each of the prescriptions.

   KEY IGNITES INVESTIGATION
    Arrested on 8/22/02
    Charged with insurance fraud, attempted grand larceny, falsely reporting an
    incident and making a punishable false written statement

    A Staten Island resident was charged with reporting his 2000 GMC stolen in an
    attempt to fraudulently collect more than $3,000 in insurance proceeds. The
    defendant claimed that he was in possession of all existing keys to the car but
    the car was recovered with an original key still in the ignition. An investigation
    by the Frauds Bureau and the NYPD Auto Squad led to his arrest.

   COUPLE IN CAHOOTS
    Arrested on 8/21/02
    Charged with insurance fraud in the 3rd degree and attempted grand larceny in
    the 3rd degree

    A Monroe County couple reported to the Webster Police Department on 1/5/01
    that their home had been burglarized and several items stolen from their garage.
    They subsequently submitted a claim to Security Mutual Insurance Company
    for about $21,000 for the bicycles, golf clubs, tools and other property
    allegedly stolen. An investigation conducted by the Frauds Bureau and Security
    Mutual turned up evidence that the receipts they submitted in support of their
    claim were fraudulent.

   "GIVE-UP" AND "BUY-BACK"
    Arrested on 8/20/02
    Charged with insurance fraud in the 3rd and 6th degrees, grand larceny in the
    3rd degree and offering a false instrument for filing in the 2nd degree

    The suspect in this case reported to the police and Allstate Insurance Company
    on 10/1/01 that his 1996 Mercedes Benz had been stolen. However, evidence
    indicated that he had "given up" his car for the insurance proceeds. The car was
    obtained on 9/21/01 in a "buy-back" sting operation conducted by the NYPD
    Auto Crime Division.

   A LONG WAY FROM HOME
    Arrested on 8/16/02
    Charged with insurance fraud in the 3rd degree, grand larceny in the 3rd
    degree, making a punishable false written statement and falsely reporting an
    incident in the 3rd degree
       A Bronx woman was arrested following a complaint to the Frauds Bureau by
       the Kemper Insurance Company on suspicion of a fraudulent claim. This
       defendant reported his 2001 Mitsubishi stolen on 3/8/02. During an
       investigation by the Frauds Bureau and the NYPD Auto Crime Division, the
       car was found to have been in the possession of the Anchorage, Alaska, Police
       Department at the time of the alleged theft.

      TORCHED IN BROOKLYN
       Arrested on 8/6/02
       Charged with insurance fraud, attempted grand larceny, falsely reporting an
       incident and making a punishable false written statement

       An electrician‘s assistant from Staten Island was accused of fraudulently
       reporting his car stolen. In fact, the car had been recovered in Brooklyn prior to
       his report when the FDNY responded to a report of a vehicle on fire. His arrest
       was the result of an investigation by the Frauds Bureau and the NYPD Auto
       Crime Division.

      REVOKED BROKER/BOGUS CARDS
       Arrested on 8/6/02
       Charged with forgery in the 2nd degree, criminal possession of a forged
       instrument in the 2nd degree, scheme to defraud in the 1st degree, falsifying
       business records and insurance fraud

       An investigation by the Frauds Bureau, the NYPD Fraudulent Accident
       Investigation Squad and the State Inspector General‘s Office into the suspected
       sale of fake insurance identification cards led to the arrest of a former insurance
       broker from the Bronx. Complaints to the Insurance Department about the
       potential sale of phony ID cards initiated the investigation. A search warrant
       was executed after an undercover purchase of an ID card that was suspected of
       being fraudulent. Further evidence in the case revealed that the defendant‘s
       broker‘s license had been revoked in 1998.

It should be noted that arrests and indictments are merely accusations and that a
defendant is presumed innocent until proven guilty.

April 26, 2010 22:51 IST
Facebook used by private investigators to uncover insurance fraud                     Email

ANI
                                                                                      Print
Monday, Melbourne: Social networking sites like Facebook are being used
by private investigators to uncover false claims made to insurance companies.         Share
   


International experts have revealed that the sites are 'gold' for identity thieves, reports
Courier Mail.

They are perusing photos and comments made on the sites of claimants and witnesses to
see if they tally with statements made to insurance companies.

In some cases investigators are uncovering photos showing people who claim to have
injuries preventing them from working doing activities such as skiing.

But sites such as Facebook also have become a tool for investigators to uncover people
doing undeclared jobs, to track down those who owe debts and uncover the shady past of
job applicants.

Investigation firm MPOL Investigations Australia has an agent dedicated to searching the
social networking sites.

Using a social networking site, the company discovered that a claimant who was
suspected of having undeclared income did have a hidden part-time job.

While the Facebook site had a privacy block, the investigators were able to search an
open "friend" site, which provided a clear link to their subject.

The investigation firm used photos on a social networking site to prove that people who
claimed their home had been broken into were at home at the time, having a party.

Julia Robson, the company's social networking specialist, said one person claiming to
have a foot injury posted family pictures showing him playing soccer.

Craig Adams of Brisbane's CA Investigations said information gleaned from social
networking sites mostly was used to gauge how much people exaggerated their claims.

He said in one case a woman who claimed she had a psychological injury and could not
socialise, posted Facebook photos of herself sitting in bars on Melbourne Cup Day.
BANGALORE: Expressing serious concern over the steep rise in fake insurance claims
relating to motor vehicle accidents in the state, Dr DV Guruprasad DGP, CID, called
upon officers of various insurance companies to be vigilant and bring fake cases to the
notice of police as early as possible.
Dr DV Guruprasad said that in the recent past, instances of bogus claims in cases of
motor vehicle accident are increasing due to the connivance of doctors, advocates,
policemen and prosecutors.
Citing the example of Mandya, he said that during 2008-09, over 350 cases of motor
vehicle accident were registered and preliminary inquiry revealed that in most cases,
accidents had not taken place at all. The complainants would lodge a false complaint
using fake medical certificates and would claim compensation in the court.
Guruprasad said that there was a pattern in such cases, wherein a number of persons
colluded to defraud the insurance companies. He requested the insurance companies to be
more alert and lodge complaints whenever false claims are suspected.
Representatives of insurance companies said that there are certain doctors who are in the
habit of issuing bogus certificates.
They said action needs to be taken against such doctors.
HC Kishore Chandra, IGP, (Economic Offences), and Malini Krishnamurthy, DGP
(Economic Offences) added that CID has already chargesheeted a number of persons
including police officers, doctors and advocates in cases of false insurance claims made
against the Karnataka State Road Transport Corporation.
Following this, insurance claims against KSRTC has reduced by over 40 pc. The
insurance companies said they would appoint a nodal officer in each district and Police
Commissionerate and work closely with the jurisdictional police to bring down such
frauds.



Health insurance claim ratio 40% higher in metros

   




New Delhi, Sept 20: The claim ratio of health insurance products is about 40% higher in
the metros and other big cities compared to tier II cities, like Jaipur and Pune. This is
primarily due to the fact that cost of medical facilities is significantly higher in bigger
cities, though consumers across the country have to pay the same premium.

There is, therefore, pressure on insurance companies from third party administrators
(TPAs) to fix premium rates on the basis of region and the average cost of medical
facilities there.

Explaining the reasons behind this trend, Icra senior analyst Vineet Nigam said in
addition to basic medical facilties, even the rate of normal consultation is higher, which
pushes up the claim ratio in metros. ―There is also a lot of movement of patients from the
smaller to the bigger towns after detection of some disorder. This also increases the claim
ratio in the metros,‖ he said.

E-Meditek Solutions director Gopal Verma added that the need of the hour was to have
different slots of premium for different regions. ―The general insurance companies which
are currently selling health insurance products must have different rates of premia for
different areas. Until that is done, there cannot be uniformity in the claim ratio,‖ he
pointed out.

It may be noted that the KP Narsimhan committee report on the insurance sector has
suggested that standalone health insurance companies should be allowed to operate. The
committee also underlined the need to bring down the minimum capital base norm from
the current Rs 100 crore.

There are currently about 25 registered TPAs in the country. The number is likely to
increase significantly once the standalone health insurance companies start operations.
Meanwhile, Insurance Regulatory and Development Authority‘s working group is also
preparing a concrete report on health insurance.



Insurance investigations are usually conducted to investigate matters pertaining to
insurance claims that are suspicious or otherwise in doubt for some reason. Investigators
in this field have differing specialities and backgrounds. Some insurance companies have
their own in-house investigation teams while other companies sub-contract the work to
private investigators or private investigation firms. Although such investigations are
usually conducted to combat fraud, very often investigators will be working simply to
establish the circumstances of a particular claim (for example, in a multi-vehicular road
accident involving various parties, claims and insurance companies).

Insurance Fraud

Methods of defrauding insurance companies are manifold, as are the means of
investigating them. As a crime, however, evidence shows that insurance fraud in wealthy
nations is increasing, with many governments running public awareness campaigns to
deter potential fraudsters and appeal to the public to report any suspicious claims.

One of the most common forms of insurance fraud is the exaggeration of injuries
sustained in an accident. For example, a claimant in a vehicle accident who sustained
genuine injuries may exaggerate their extent, their effect on his ability to work or enjoy
life and the length of time it takes for the injuries to heal. Such exaggerations are made
with the intention of receiving a higher amount of money. Because many injuries can be
exceptionally difficult to quantify (for example, psychological injuries or physical
injuries such as whip-lash), investigators will often seek to establish that what the
claimant claims is true (for example, if a claimant states he or she cannot work) and that
there are no obvious discrepancies in the symptoms claimed (very often examined in
conjunction with medical staff). Surveillance is often employed in such circumstances to
verify the claim.

Another form of lesser known fraud is that of claiming on an insurance policy for injuries
sustained before the policy came into effect. For example, in a road accident, a person
may claim to have sustained a debilitating back injury. On investigation, however, it
transpires that the injury had been sustained in an incident some months or even years
before. Very often insurance companies and investigators will study medical reports and
history to eliminate this possibility, as well as searching for evidence of previous claims
or accidents.

There are also many forms of fraud involving property, some of which have garnered
more attention in the media due to higher monetary value on the policies. An example
would be a person with valuable assets (property, for example) who deliberately destroys
them, often through arson, with the intention of then claiming the value back through
insurance. Another form would be an art collector insurance a high value piece and then
having it 'stolen' - claiming the money for himself and keeping the art piece in the
process.

				
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