Fighting Insurance Fraud with FICO Identity Resolution Engine How Insurers Can Uncover More Fraud with Insight on Individuals and Criminal Rings

Transcription

1 white paper Fighting Insurance Fraud with FICO Identity Resolution Engine How Insurers Can Uncover More Fraud with Insight on Individuals and Criminal Rings July 2013»» Summary Fraudulent individuals and criminal rings plague the property & casualty and life insurance industries with a variety of schemes resulting in significant fraudulent claims payouts. Insurers rules management systems and claims scoring models help identify and stop payment on some of these claims through analysis of data on the claims themselves. However, another rich source of data has been largely untapped by the industry: personal attribute data on claimants, and on other individuals related to the claim or to the claimant. Now, with FICO Identity Resolution Engine, insurers can strengthen their current analysis of claims with an added perspective on risk associated with individuals. Applied reactively in regular claims processing, or proactively in off-line reviews, the technology uniquely gives insurers a seamless, deeper view into fraud risk with advanced data access capabilities, identity resolution algorithms, link analysis technology and visualization tools. Make every decision count TM

2 »Introduction:» Fighting Fraud with a Focus on Individuals and Fraud Rings As much as individual and organized fraud costs the insurance industry, no data should be left unexplored. That s particularly true in light of the real, cumulative cost of fraud in terms of direct, but also indirect, losses. As for the direct losses, many insurers estimate that between 10% and 20% of claims are fraudulent, and that they detect or deny less than 20% of those fraudulent claims. In other words, up to 15% of all paid claims results in lost money. Simply from a balance sheet perspective, reducing that by even a few percentage points could have a significant, positive impact to profitability. But consider other ways fraud reduction could add to long-term profitability. For example, even minimally cutting fraud losses could help insurers lower premiums in some cases and attract more business in today s highly competitive marketplace. In addition to direct losses and lost opportunities for growth, fraud drives losses in another, sometimes overlooked, way attrition. Fraud, by its inherent, constant threat, challenges the speed and efficiency of many firms claims handling, which in turn can affect customer retention. Slow claims payments or worse, an erroneously denied claim are a major factor for customers not renewing policies. With today s social media, that can snowball quickly into fast and widespread reputational damage. Insurers therefore face a balancing act between trying to expedite claims reviews and processing and simultaneously identifying and stopping more fraud. So how can insurers meet this challenge? Recently, a major insurer in the United Kingdom implemented FICO Identity Resolution Engine to supplement its use of analytic models to detect claims fraud. Within weeks, the insurer was able to realize a 30% increase in fraud detection over using analytics alone. The answer lies partly in Big Data. Insurers must tap into today s wide variety of data sources to gain deeper insight into fraud its patterns and perpetrators. But to many insurers, that sounds like a recipe for even slower claims processing. How much time and IT resources requirements will it take to link to new data sources, and how quickly can data from multiple sources be accessed and delivered? FICO is helping insurers overcome that obstacle and add an important dimension to fraud detection analysis with its introduction of FICO Identity Resolution Engine. With this recent addition to FICO Claims Fraud Solution, insurers can begin to complement their analysis and rules management of claims data by tapping into data on claimants, as well as other third-party individuals tied to claims quickly and cost-effectively. While there s proven value in technology that helps evaluate the likelihood of a claim being fraudulent, identifying suspicious information on individuals related to claims can lead directly to a strong reduction in losses from organized fraud rings as well. This paper examines how FICO Identity Resolution Engine can work in conjunction with insurers business rules management and analytic fraud detection models to improve identification of organized fraud, and therefore increase overall fraud detection and prevention rates, in some cases by as much as 30%. It discusses: The extensive availability of data on fraudulent individuals and fraud rings, and its unique value in uncovering organized fraud perpetrations. How FICO Identity Resolution Engine draws insight into suspicious behavior from data on claimants and other individuals. A fictitious review of a claim by an investigator (see section titled The Four Core Functions of FICO Identity Resolution Engine ) as a means to describe the essential features and functionality of FICO Identity Resolution Engine and how they provide maximum protection and operational efficiency in fighting organized fraud. A Trail of Suspicious Data It s understandable for insurers to see fraud as a daunting problem, considering fraudulent individuals and rings expansiveness and creativity in modifying schemes. However, insurers must also focus on what those same characteristics of fraudsters leave behind: a great deal of evidence Fair Isaac Corporation. All rights reserved. page 2

3 With multiple individuals across multiple entities involved in ongoing fraudulent claims, organized fraud rings fingerprints can be found across disparate databases in the form of personal attribute information. For example, as members of a ring attempt to defraud various carriers, they use and leave behind fictitious and shared personal information such as addresses, phone numbers and license numbers. While the bad news is that every major carrier experiences numerous types of organized fraud on a daily basis, the good news is that data on many of the participating con artists is captured. Now, with FICO Identity Resolution Engine, insurers can identify more fraudulent claims prior to payment by piecing together data for a composite of who s who (to determine that an individual is using multiple, various versions of personal attribute information an indication of a fraud perpetrator) and who knows whom (to uncover links between disparate individuals sharing the same personal attribute information an indication of a possible fraud ring). In a typical day, fraudulent claims across a variety of fraud schemes can generate data on a high number of participating fraudsters. But without a matching technology, it would be difficult to resolve identities and to identify connections between parties across disparate databases. For example, in the realm of automobile collision fraud, consider how many fraudulent individuals may be involved in or associated with a claim. In addition to the claimant, staged or induced accidents may involve fictitious riders claiming whiplash, phony witnesses, as well as dishonest doctors, lawyers and repair shop operators. Medical mills can also involve numerous fraudsters across diverse roles. There s no better example than the recent bust of a New York auto fraud ring that netted $400 million in bogus auto injury claims, recruiting scores of patients (with the offer of payment) from accident scenes and sending them to fraudulent doctors in clinics for treatments. With FICO Identity Resolution Engine, data on all these perpetrators can be examined to identify suspicious individuals as well as suspicious connections between those individuals. An Added Perspective for Detecting More Fraud FICO Identity Resolution Engine isn t meant to replace existing approaches to detecting fraud. Rather, as an adjunct to an insurer s current practices, it provides a different perspective that can increase overall fraud detection rates. In addition, it can help carriers lower operational costs and improve customer service and retention by reducing false positives an enormous problem for the industry. Analysis from FICO Identity Resolution Engine can be strategically applied based on the findings of insurers rules management technology, analytic models or a combination of both. With a focus on the claimant rather than the claim, insurers can apply Identity Resolution Engine: In reactive investigations in claims processing to support payment decisions on claims in the grey area claims that rules or analytic models identified with only a slight degree of fraud likelihood or to evaluate claimants and related individuals on claims that rules or models found to have a high likelihood of fraud as a method to prevent further abuse by identifying and blacklisting suspicious individuals. To uncover additional fraudsters after catching one perpetrator in a fraud attempt. In a proactive mode, searching for suspects by performing broad-based searches of various individuals. To help Special Investigative Units prioritize work and keep costs under control, and to more aggressively pursue claims in their work queue linked by suspicious claimants and individuals. To help identify fraudulent rings related to claims which were previously paid, to support recovery efforts Fair Isaac Corporation. All rights reserved. page 3

4 »» Knocking Down Barriers to Accessing and Understanding Data About Criminal Rings and Fraudulent Individuals To leverage the value of data on fraud ring perpetrators as well as fraudulent individuals, it s essential for insurers to quickly access as many internal and external data sources as possible, and forensically match the data attributes that serve as the golden nuggets of financial crime prevention and investigation. In terms of underlying technological power, the key to realizing a meaningful reduction of losses from criminal fraudsters is two-fold: 1. Fast and resourceful access to a breadth of data to be able to conduct thousands of queries per day on hundreds of millions of records across dozens of disparate databases. 2. The ability to understand the identity matches and non-obvious relationships between individuals across dozens of data sources despite input errors or deliberate attempts to deceive. Seamless, Widespread Data Access The more an insurer can expand its breadth of analysis across data sources, the more success it will have in uncovering matches and relationships. However, widespread data access has presented a major time, expense and resource challenge for insurers attempting to implement an effective link analysis solution. Many vendor solutions require the insurer to develop a separate data warehouse, move data into a common repository, and cleanse and normalize the data. After the drain caused by the initial set up, of course the insurer must invest further time and resources for importing data upgrades. FIGURE 1: SINGLE SEARCH ACCESS TO MULTIPLE DATABASES Data Sources Investigator Request Link Analysis Customer Employee Claims Negative Case Manager Matches Third Party Agent Applicant With FICO Identity Resolution Engine, an insurer s investigators can access an unlimited number of internal and external databases simultaneously, significantly expediting their triage of cases. Rather than having to import data from multiple sources into a separate repository (and cleanse and format data), the technology allows investigators to look at data in the original location and source format. Investigators can perform a single search into multiple databases, eliminating the need to successively log on and off separate databases. FICO Identity Resolution Engine turns this problem around. With sophisticated search technology, investigators eliminate the need to import and prepare data. That means they can review data across a wide variety of internal or external data sources including commercial sources without the exorbitant costs, time delays and IT resource drain required by other methods. In addition, the solution s advanced data search technology gives investigators simultaneous access to multiple data sources (see Figure 1). Rather than logging on and off of each data source in succession for example, individually searching separate data sources on customers, agents/employees, claims, negative data watch lists or external thirdparty databases the search technology accesses all data sources simultaneously. This enables investigators to triage cases in minutes or seconds, rather than days or weeks Fair Isaac Corporation. All rights reserved. page 4

5 In addition, FICO Identity Resolution Engine benefits insurers in two other important ways: Avoiding privacy issues. By not moving data from various sources into a central repository, insurers avoid the possibility of commingling sensitive data. They also eliminate the risk of being responsible for making sensitive data public via database attacks or unintentional distribution of data. The forensic value of data is not jeopardized. Many data matching systems discard valuable forensic data as part of the data cleansing process. For example, if an insurer determines that a John Doe is an alias and the correct name is Jon Doe, many systems will automatically discard all John Doe references after the determination is made, thereby diminishing the forensic value of data searches. Unlike other data matching options, however, FICO Identity Resolution Engine retains all of the correct information so there s no rework when the case gets turned over to litigation. Expanding the Power of Analytics to Ring Detection Fluid access to massive amounts of personal attribute data must be followed by the ability to analyze the data for fast, insightful revelations leading to suspicion of fraud, and fraud ring activity. With fraud s pervasiveness, and with so much data to tap into, insurers should deploy all detection technologies at hand. FICO Identity Resolution Engine provides identity resolution and relationship discovery algorithms also referred to as similarity search algorithms to supplement insurers use of rules systems and claims models. The solution offers dozens of different similarity search algorithms to accomplish various detection objectives. The algorithms are designed to: Identify hidden relationships between claimants, insureds, witnesses, service providers, addresses, vehicles and property. Far exceed exact-match/fuzzy logic capabilities to uncover previously hidden identity information. Detect misspellings or intentional data manipulations to reduce false negatives. Uniquely be applied across multiple attributes to disregard irrelevant matches and reduce false positives that waste valuable investigative resource time. Provide alerts to fraud indicators at first-notice-of-loss.»» The Four Core Functions of FICO Identity Resolution Engine FICO Identity Resolution Engine provides four core functions for insurers. In just minutes or seconds, the technology helps investigators: 1. Determine if a claimant is using multiple, slight variations of personal attribute information, and is therefore deemed suspicious of fraud. 2. Find links between disparate individuals sharing the same personal attribute information to uncover possible fraud rings. 3. Visually analyze matches and relationships on-screen, and rely on the visual representation in court proceedings. 4. Receive red flag alerts of data matches when transactions occur Fair Isaac Corporation. All rights reserved. page 5

6 Today, meaningful reduction in organized fraud is being realized by P&C insurers that combine social link analysis technology with rules management and claims scoring platforms to achieve important synergies. Advances in social link analysis technology make it a powerful tool to take advantage of the rich insight on risk that can be derived from personal attribute information. Mike Fitzgerald, Senior Analyst, Celent The best way to fully appreciate the ability of these four core functions to uncover a fraudulent ring or individual is to experience their use from the perspective of an investigator. Imagine that an investigator has received a case that is deemed potentially fraudulent following rules management or claims fraud detection modeling. With FICO Identity Resolution Engine and therefore with the ability to gauge risk from a different perspective the investigator would take advantage of the solution s four core functions in the following way. Cross Database Identity Resolution The case the investigator has received is for an auto collision claim, with the claimant (covered by another insurer) seeking collision and bodily injury reparation payments from the investigator s firm. The claim finds the other driver, a policyholder with the investigator s firm, at fault, having rear-ended the claimant s vehicle while both cars were entering a roundabout. The claimant claims another car cut him off, forcing him to make a sudden stop and resulting in the policyholder slamming into the back of the claimant s vehicle. The claimant had three other passengers in the vehicle, and the claim includes medical reparations for whiplash for all three. The policyholder, meanwhile, has formally submitted a statement that he did not see any other vehicle cut off the claimant s vehicle. However, the claimant was able to identify and engage the testimony of two witnesses standing on the side of the roundabout. The investigator must answer: Was the accident legitimate and un-coerced, or was it a fraudulent staged situation, a crash for cash accident? FICO Identity Resolution Engine provides a viable approach to find out in fact, without it the investigator s options are limited. The first step an investigator with the FICO solution would take is in investigating the claimant to resolve his identity: Is this claimant someone who appears, perhaps with slight variations to personal attribute information, on other claims either as a claimant or as another third party related to a claim? If so, how strong are the matches? Can they be relied upon to deem the claimant suspicious, and to stop payment to the claimant? FICO Identity Resolution Engine s cross database identity resolution quickly answers these questions. It determines who s really who by finding, matching and linking similar people across disparate data sources. With the solution s advanced technology for providing seamless access to numerous internal and external data sources, as discussed earlier, the investigator can perform a simultaneous search across as many databases as desired looking for personal attribute similarities on the claimant. The solution s similarity search algorithms overcome data barriers posed by clerical errors, linguistic differences and purposeful misrepresentations that otherwise would make it difficult for typical investigative tools to find similar, but not exact, matches across databases. The algorithms accurately find matches on people, places and things, even if they are only partially similar. In addition, the strength of the similarity of matches is also presented to investigators. For example, in the investigator s case the claimant s name is John Smith (see Figure 2). Three other matches are found with similar spellings on his name, his address and his driver s license number. FICO Identity Resolution Engine will not only display potential matches, but also score the matches in terms of their likelihood to be attributed to the same individual. Based on matches and scores from the investigator s identity resolution search, the investigator concludes that the claimant is a suspicious individual. The next step, therefore, is to determine if the claimant shares attributes with other individuals, either those already on blacklists or any number of other data sources. Again, FICO Identity Resolution Engine facilitates this level of sophisticated searching with the ability to simultaneously access multiple databases at once Fair Isaac Corporation. All rights reserved. page 6

7 FIGURE 2: IDENTIFYING FRAUDULENT INDIVIDUALS, RELATIONSHIPS AND RINGS Johnathan Smith 1 Thalia St Tel# DOB 07/08/64 LIC# SIU Data Applicant Data Claim Data Policyholder Data Relationship John Smith th Avenue Tel# LIC#170UYRE8364 DOB 07/09/66 Individual Potential Fraud Ring Jian Smythe 32 Fraser Lane Cell# LIC#17-OUYRE-*8364 DOB 07/09/63 R. Jean Smith One Thaliea St Tel# LIC# Relationship This graphic shows how FICO Identity Resolution Engine performs cross database identity resolution and link analysis. When a claim is filed in this case by Jian Smythe identity resolution algorithms can first determine whether an insurer should be suspicious of the claimant by analyzing the claimant s personal data across multiple databases. If the claimant appears to be a likely fraud perpetrator, based on suspicious links across databases, link analysis can then search for attribute matches such as phone numbers or addresses between the claimant and individuals in other databases to find connections that may indicate a fraud ring. Mr. Reid Hopine Ms. Cathryn Lawley 1065A Sixth Ave Tel# Previous Address: 3245 S. Bluff Blvd, Clinton Relationship 3245 South Bluff Cell# Bank Branch 1034 DOB 07/14/75 3 rd Party Risk Data Adjuster Data Link Analysis Since the investigator has found reason to suspect the claimant of possible fraud, the next step might be to investigate the claimant s relationships with individuals listed on the claim. A logical start would be to search for matches to the passengers and the witnesses listed on the claim. As shown in Figure 2, in investigating Mr. Reid Hopine, a passenger who has allegedly suffered whiplash in the accident, a simultaneous search of multiple databases might find one or multiple listings across disparate sources. In this case, Mr. Hopine was found in a 3rd Party Risk database. More importantly, the FICO Identity Resolution Engine s similarity search algorithms found that the claimant and Mr. Hopine share a highly similar address in one match. Assuming the address match received a high score for its strength-of-match, the investigator is now beginning to build a strong case against the claim. And had the search resulted in multiple or several matches of shared attributes addresses, former addresses, phone numbers, account numbers, etc. between Mr. Hopine and the claimant, the investigator s case would be further reinforced Fair Isaac Corporation. All rights reserved. page 7

8 The investigator would also perform a search for information and attribute matches with one of the witnesses listed on the claim in this case a Ms. Cathryn Lawley. At least one database, the insurer s internal Adjuster database, found that Ms. Lawley s cell phone number matches one of the cell phone numbers of the claimant s aliases. At the same time, the search algorithms found that the passenger, Mr. Hopine, and Ms. Lawley share an address (listed as Mr. Hopine s former address). The claim is now beginning to look highly suspicious of fraud perpetrated by a ring. Taking this example a step further, had an estimate for repairs been submitted on the claim from an auto body shop, or had a doctor s report been submitted describing the passengers injuries, the search algorithms would also be able to look for attribute matches between those individuals and the claimant, passengers or witnesses to identify possible collusion. Another way FICO Identity Resolution Engine can search for relationship matches is by using attributes other than individual names. For example, the phone number of the claimant, passenger or witness might be linked to one or several other individuals found across a variety of disparate internal and external databases. Insurers can take advantage of this functionality to broaden cases, and in some cases, to identify insider fraud based on matches to employees. Real-Time Visualization Investigators need to work quickly. That means they don t have time to read print-outs of results of searches and matches, and then piece together the relationships and the degrees of separation between the relationships. FICO Identity Resolution Engine provides a strong visualization component to graphically display relationships and degrees of separation. In minutes or seconds, as the solution searches multiple data sources simultaneously for matches, it gives investigators an on-screen graphic display of the matches that it has determined as suspicious. Its icons identify individuals and, with each individual displayed, lists key matches of personal attribute information between individuals. For example, in the case of the staged accident investigation, the visualization tool can show identity resolution matches between a single individual the various aliases of Johnathan Smith found across multiple databases and the relationship matches showing shared personal attribute information between individuals the links between the passenger, Mr. Hopine, and the claimant; the witnesses, Ms. Lawley, and the claimant; and between the passenger and the witness. The visualization functionality also displays up to four degrees of separation, giving investigators a broader perspective on the scope of a possible ring s relationships. Visualization is also highly beneficial in legal proceedings. It can be used to quickly and clearly show prosecutors and other legal officials suspicious information between individuals. Red Flag Alerts FICO Identity Resolution Engine can also help a carrier prevent fraud losses in near real-time by automatically displaying suspicious activity directly to the investigator as transactions occur such as at First Notice of Loss. In addition, it can support fraud fighting at the enterprise level, across lines of business. For instance, in the staged accident example, had the auto investigator s identity resolution search on Johnathan Smith, or the relationship matching search on Reid Hopine or Cathryn Lawley, found a match in one of the carrier s homeowner policy databases, the solution could automatically alert the homeowner business unit of the match. Alerts provide a means for sharing important insight on potential fraud activity that can significantly reduce a carrier s overall losses Fair Isaac Corporation. All rights reserved. page 8

9 »» Conclusion Data is information that, when properly examined, can provide insight that drives beneficial action. The insurance industry needs to take additional actions to stop the significant problem of losses caused by individual fraudsters and organized crime rings. One way insurers can do so is by taking advantage of a vast amount of data that, to date, has gone largely unexplored. Personal attribute data on claimants and other individuals can be found across a variety of data sources much of it within insurers own databases, as well as accessible external databases. Today, with FICO Identity Resolution Engine, insurers can begin to quickly derive highly beneficial insight from personal attribute information that can drive appropriate action to stop payment on claims stemming from organized fraud rings. The addition of FICO Identity Resolution Engine is the next logical complement to insurers current claims decision-making processes.»» About FICO FICO (NYSE: FICO) is a leading analytics software company, helping businesses in 80+ countries make better decisions that drive higher levels of growth, profitability and customer satisfaction. The company s groundbreaking use of Big Data and mathematical algorithms to predict consumer behavior has transformed entire industries. FICO provides analytics software and tools used across multiple industries to manage risk, fight fraud, build more profitable customer relationships, optimize operations and meet strict government regulations. Many of our products reach industry-wide adoption such as the FICO Score, the standard measure of consumer credit risk in the United States. FICO solutions leverage open-source standards and cloud computing to maximize flexibility, speed deployment and reduce costs. The company also helps millions of people manage their personal credit health. Learn more at For more information North America toll-free International web (0) info@fico.com FICO and Make every decision count are trademarks or registered trademarks of Fair Isaac Corporation in the United States and in other countries. Other product and company names herein may be trademarks of their respective owners Fair Isaac Corporation. All rights reserved. 2897WP 06/13 PDF