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. 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. 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. 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 Woman arrested for murdering husband's lover...Escorts' Anil Nanda faces murder rapCBI unearths Railway recruitment scam, RRB c...Man who forced wife to group sex sent in jud...Sikh group asks US lawmakers to stop racial ...Social stigma forces many to accept bigamy: ... 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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