The Use of Analytics for Claim Fraud Detection Roosevelt C. Mosley, Jr., FCAS, MAAA Nick Kucera Pinnacle Actuarial Resources Inc.

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1 Paper The Use of Analytcs for Clam Fraud Detecton Roosevelt C. Mosley, Jr., FCAS, MAAA Nck Kucera Pnnacle Actuaral Resources Inc., Bloomngton, IL ABSTRACT As t has been wdely reported n the nsurance trade news, fraudulent clams contnue to be a sgnfcant ssue n the nsurance ndustry, costng polcyholders bllons of dollars. More companes are turnng to analytcs to help dentfyng clam fraud. Identfyng clam fraud usng predctve analytcs, however, represents a unque challenge. 1. Most predctve analytcs applcatons have a complete target varable whch can be analyzed. Fraud s unque n that there s generally a lot of fraud that has occurred hstorcally that has not been dentfed. Therefore, the defnton of the target varable s not complete. 2. There s a natural assumpton that the past wll bear some resemblance to the future. In the case of fraud, methods of defraudng nsurance companes change quckly and t can make the analyss of a hstorcal database less valuable for dentfyng future fraud. 3. In an underlyng database of clams that may have been determned to be fraudulent by an nsurance company, there are nconsstences between dfferent clam adjusters regardng whch clams are referred for further nvestgaton. These nconsstences can lead to hstorcal databases that are not complete. Ths paper wll demonstrate how analytcs can be used to help dentfy fraud and allow an nsurer to optmze the resources they have avalable n combatng ths fraud. Applcatons dscussed nclude: 1. More consstent referral of suspcous clams to clam nvestgatve unts 2. Better dentfcaton of suspcous clams, even as technques used to defraud nsurers are changng 3. Incorporatng clam adjuster nsght nto analytcs results to mprove the process As part of ths paper, we wll demonstrate the applcaton of several approaches to fraud dentfcaton: 1. Clusterng 2. Assocaton analyss 3. PRIDIT (Prncpal Component Analyss of RIDIT scores) INTRODUCTION In the property and casualty nsurance ndustry, there are many reports that the occurrence of clam fraud s ncreasng, and t s evdent that the focus on clam fraud has been magnfed. Many of the estmates of the amount of clam fraud n the ndustry show ths trend, and one only has to follow an nsurance news feed to know that the amount of reportng that s related to clam fraud seems to be rsng sgnfcantly. Regardless of the exact 1

2 amount of fraud present n property and casualty nsurance, all agree that t s a sgnfcant amount and thus a concern that needs to be addressed. Insurance companes have developed effectve procedures for dentfyng, nvestgatng, and deterrng fraudulent actvty. The combnaton of experenced clam adjusters and specal nvestgators has produced a process that ensures clam payments beng made are far and legtmate. The experence, nsght, and ntuton of these clams personnel have saved nsurance companes mllons of dollars n payments over the years. However, as good as experenced clam adjusters and specal nvestgators are, the realty s there are not enough of these traned eyes to revew every clam, and thus there are some fraudulent clams that slp through the cracks. As a result, payments are sometmes made that should not be. Predctve analytcs can assst nsurance companes n developng a more consstent clam referral process, such that the beneft of the expertse of the best adjusters and nvestgators s appled to all clams. Predctve analytcs can also enhance the work of the clams department by uncoverng complextes and nuances n a partcular clam that may be mssed by even the most experenced clam adjusters. THE CLAIM FRAUD PROBLEM The problem of clam fraud by any measure s a large one. Ths can be seen n fraud statstcs that are tracked by dfferent organzatons and the fnancal mpact t has on the ndustry. The Natonal Insurance Crme Bureau (NICB) produces a quarterly report on questonable clams reported to NICB by nsurance companes. Based on the latest report, the number of questonable clams for the frst three quarters of 2011 as compared to 2009 and 2010 are shown below 1. N u m b e r o f C l a m s 100,000 90,000 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10,000 - Questonable Clams 93,053 87,684 74,944 Q1 - Q3, 2011 Q1 - Q3, 2012 Q1 - Q3, Fenng, Davd. Frst 3 Quarters of 2011, 2012, 2013 Questonable Clam Referral Reason Analyss (Publc Dssemnaton). NICB Data Analytcs Forecast Report. October 29,

3 As can be seen from the chart above, the number of questonable clams has ncreased sgnfcantly n 2012 (17.0%) and 2013 (6.1%). The Coalton Aganst Insurance Fraud also conducted a study of consumer atttudes related to nsurance fraud. The problem of fraud s hghlghted n some of the key fndngs from ths study 2. 1 n 5 adults thnk t s acceptable to defraud nsurance companes 1 n 10 people thnk t s OK to submt clams for tems that are not lost or damaged, or for an njury that ddn t occur 16% of people thnk t s OK to nflate a clam to cover the deductble These types of consumer atttudes compound the problem, and are further confrmaton that fraud s a sgnfcant ssue for nsurance companes. Fraud costs nsurance companes, and ultmately consumers, a sgnfcant amount of money. The Coalton Aganst Insurance Fraud estmates that fraud and buldup added $4.8B to $6.8B to auto nsurance costs n In worker s compensaton, fraud cost nsurers over $550 mllon dollars n In healthcare, $68B s lost to fraud every year 5. In total, the Coalton estmates that $80B s lost due to fraudulent clams every year. Wth bllons of dollars at stake, even a mnmal ncrease n fraud detecton or preventon wll translate nto substantal dollar savngs for nsurance companes and ther customers. THE FRAUD DETECTION PROCESS Gven the sgnfcant cost, companes have developed processes to combat nsurance fraud. A typcal fraud detecton process s shown below: 2 Coalton Aganst Insurance Fraud. Four Faces: Why Some Amercans Do And Don t Tolerate Fraud. October,

4 Identfy Refer Investgate Resolve Fgure 1: Insurance Company Fraud Detecton Process The frst step n the fraud detecton process s the dentfcaton of a suspcous clam. Hstorcally, there have been two man ways that these clams have been dentfed. The frst has been to rely on adjusters to dentfy potentally fraudulent clams. The second has been by usng a set of fraud ndcators to assst the clam adjuster n determnng f the clam s suspcous and warrants addtonal scrutny. One way an adjuster dentfes a suspcous clam s by recognton of a suspcous pattern that has been seen prevously. An example of ths could be a repeat offender, or f the same medcal provder, attorney, and patent show up together repeatedly n dfferent clams. Ths ablty s heavly dependent on the experence of the clam adjuster, and also on access to advsory clam databases where nformaton on hstorcal clams can be researched and the current clam valdated n lght of ths hstorcal nformaton. Ths approach to dentfyng suspcous clams has some dsadvantages though. Ths assumes that the nvestgator has seen ths type of fraud n the past, so t wll be heavly dependent on the experence level of the adjuster. Also, as fraudsters adapt and become smarter (.e. usng alases, ncludng dfferent ndvduals n a fraudulent network), repeat patterns wll be more dffcult to recognze. Another approach that adjusters use to dentfy suspcous clams s smply applyng ther experence and ntuton. Generally, f somethng smells funny to a seasoned adjuster about a clam, t can be a great ndcator that there s a potental ssue wth the clam. It s at ths pont that the clam s then referred to the specal nvestgatve unt (SIU). Ths approach reles as well on the experence of the adjuster to dentfy suspcous clams. Because of ths, the obvous drawback s that adjusters wth less experence may not be able to detect that the clams are suspcous. To address the ssue of consstency, some companes supplement the experence of the adjuster wth fraud ndcators. These are rules developed by the company that determne f a clam should be referred for further nvestgaton. Ths approach can be useful n dentfyng known and typcal fraud scenaros. There are a few advantages to an approach lke ths. These advantages nclude the facts that t s easy to mplement and modfy, t s easy to understand, t s effectve n attackng specfc problems, and when mplemented, produces consstent results. However, there are dsadvantages to ths approach as well. Frst, t does not help n detectng new and unknown fraud scenaros. It also has the addtonal problem of creatng smarter fraudsters as they attempt to crcumvent the fraud ndcators. 4

5 Example of fraud ndcators are: Dstance between clamant s home address and medcal provder Multple medcal opnons/provders Certan njury types (e.g., soft tssue) Changng provders for the same treatment Hgher than average number of treatments Abnormally long tme off for a gven type of njury Loss payments that do not correlate wth the severty of the njury Based on these fraud detecton methods, suspcous clams are then referred to the SIU for further nvestgaton. However, the ssues wth the clam dentfcaton methods create challenges for the SIU. 1. The clams whch are referred for further nvestgaton can be nconsstent, whch s gong to be heavly dependent on the adjuster that s handlng the clam. 2. The clam dentfcaton process can produce a sgnfcant number of false postves. False postves result n less than effcent use of nvestgaton resources. 3. Clam adjusters may not be aware of all the potental suspcous relatonshps, and therefore may mss some clams that really should be nvestgated further. 4. Because not all fraudulent clams have been dentfed hstorcally, t s dffcult to dentfy these mssed patterns gong forward. 5. There have been cases where a clam adjuster has been nvolved n the scheme to defraud an nsurer. Obvously, n a case lke ths relyng on an adjuster to dentfy fraud wll not work. 6. After suspcous clams are dentfed, t can be very dffcult to prortze these clams. Prortzaton s an mportant ssue snce most SIU s are not adequately staffed to nvestgate every suspcous clam dentfed. Predctve analytcs can be used to address the concerns wth hstorcal clam fraud processes and to optmze the resources a company has at ts dsposal. USING PREDICTIVE ANALYTICS TO DETECT SUSPICIOUS CLAIMS There are multple ways n whch predctve analytcs can be used to detect suspcous clams. 1. Analyss of Hstorcal Referrals 2. Analyss of Hstorcal Fraudulent Clams 3. Identfcaton of Networks 5

6 4. Identfcaton of Suspcous Clam Patterns 5. Combnaton of Analytcs and Adjuster Experence Analyss of Hstorcal Referrals Ths frst analyss allows nsurance companes to apply analytcs for more consstent clam referrals to the SIU. For ths analyss, the target varable s a hstory of clam referrals to the specal nvestgatve unt. Ths target can be adjusted to just use the referrals of those clam adjusters that are consdered the most experenced or most effectve. The ndependent varables consdered n the analyss are the detals of the clams. The result of the analyss s, based on the hstorcal clam referrals, the lkelhood that a new clam should be referred to the nvestgatve unt. The chart below shows the partal results of a decson tree based on an analyss of a database of automoble nsurance bodly njury coverage closed clams. The dataset ncluded an ndcator f the clam was referred to an nvestgatve unt, and also ncluded the detals of the clam (njury detals, accdent severty, clamant characterstcs, locaton, and payment nformaton). Fgure 1: Decson Tree - Predcted Clam Referral It can be determned from ths analyss whch factors were assocated wth ncreased clam referrals. These characterstcs ncluded the severty of the njury, the locaton of the accdent, and whether the clamant was represented by an attorney. Whle the decson tree results are shown here, three separate predctve models were developed: Decson Tree, Neural Network, and Logstc Regresson. Ultmately, the predcted referral was determned by an Ensemble model whch used the combned effect of the three models to produce a more accurate predcton. The dstrbuton of the predcted lkelhood of referral for the clams n the bodly njury dataset s shown below. 6

7 Referral Score D s t r b u t o n o f C l a m s 18.0% 16.0% 14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% Lkelhood of Clam Referral Fgure 2: Predcted Lkelhood of Clam Referral Based on ths dstrbuton, a cutoff score could be chosen such that clams above a certan pont would automatcally be referred to the nvestgatve unt. These clams could be sent drectly to the SIU, allowng experenced clam adjusters to spend ther tme on those clams the model classfes as beng borderlne n regards to fraud lkelhood. Analyss of Hstorcal Fraudulent Clams The analyss of hstorcally fraudulent clams relates to clams that were not just referred to an nvestgatve unt, but those clams on whch acton was taken. Ths acton can nclude a lower clam payment, the denal of a clam, and/or the referral of a clam to law enforcement authortes. So the target s whether acton was taken on the clam, and the ndependent varables are the same as those used for the referral analyss. From the same database, we analyzed the lkelhood of acton beng taken on the clam. Not only dd we revew the predcted lkelhood of acton, but we also compared the results of the predcted fraud model to the predcted referral model. Ths allows nsurers to understand the dfference n the varables that drve referrals vs. acton, and also to refne the referral process to reduce false postves and ncrease the focus on the true fraudulent clams that may have been slppng through the cracks. The table below shows a comparson of the varable mportance statstcs from the decson trees from the actonable analyss and the referral analyss. 7

8 Actonable SIU Referral Varable Importance Importance Rato Central Cty % Replace:Clamant's state of resdence % Impact severty to clamant's vehcle % Was clamant represented by an attorney? % Polcy coverage lmts per person % Arbtraton % Most serous njury % Who reported njury to nsurer % Most expensve njury % DRAGE % Lawsut status % Drver, other volaton % Amount Spent on Medcal Professonals % In ths table, t can be seen that for many of the varables, the mportance s consstent between the two analyses. However, there are some of the varables where the mportance s not consstent. For example, where the accdent occurred (central cty) s the most sgnfcant varable n the actonable analyss, however t s sgnfcantly less mportant n the referral analyss. Therefore, ths may be an area that needs to be gven more emphass n the clam department. There are also varables that were mportant n the actonable analyss, but showed no mportance n the referral analyss (lawsut status, drver volaton). Ths may pont to areas that the clam department needs to consder n ther referral process. The chart below shows the dfference between the predcted referral lkelhood and the predcted actonable lkelhood. 8

9 Referred Mnus Actonable 50.0% D s t r b u t o n 45.0% 40.0% 35.0% C l 30.0% a 25.0% m s 20.0% 15.0% 35.7% 43.5% o f 10.0% 5.0% 0.0% 0.0% 0.0% 0.0% 0.1% 0.2% 0.4% 0.3% 0.2% 3.9% 5.9% 3.2% 2.2% 1.8% 1.2% 0.2% 0.8% 0.2% 0.2% Dfference (Referred - Actonable) As can be seen from the chart above, the predcted referral lkelhood and the predcted actonable lkelhood are consstent for the majorty of the clams. However, there are clams on both ends of the scale that should be nvestgated further. On the left sde of the chart are clams wth a predcted actonable lkelhood that s sgnfcantly hgher than the predcted referral lkelhood. Ths represents clams that were not lkely to be referred hstorcally but should have been. The rght sde of the chart represents clams wth a referral lkelhood sgnfcantly hgher that the actonable lkelhood. These clams represent false postves, those clams that have been referred but turned out to be legtmate. Investgaton of these clams drans a company s resources and represents an opportunty cost to the company s SIU. The results of ths analyss can be used to refne the clam referral process and ncrease overall effcency and effectveness. Assocaton Analyss Clam fraud can also be dentfed by determnng suspcous assocatons n a clam analyss. There are a number of people assocated wth the clams process, ncludng the nsured, the clam adjuster (ether nternal or external), and any clam servce provder (medcal provder, auto repar shop, home repar contractors, etc.). There may be certan networks that show up consstently that rase suspcons and thus need to be nvestgated further. Ths can be analyzed n SAS Enterprse Mner usng the Assocaton Analyss. Assocaton Analyss has ts background n market basket analyss. It s used n retal envronments, such as grocery stores or pharmaces, to dentfy tems that tend to be purchased together. Ths analyss determnes the lkelhood of a combnaton of tems occurrng together as well as a confdence around the projecton. Ultmately, the assocaton analyss produces a set of f-then rules (f tem A s present n a transacton, then tem B wll be present as well), and the lft 9

10 assocated wth the rule. Appled to clam analyses, the tems would represent those nvolved n the clam process, and the clam would be equvalent to the transacton. The chart below shows the Assocaton Map that results from an analyss of the clamants, clam adjusters, and repar provders for a homeowners clam analyss. In the bottom left corner, there are a seres of nter-connected partcpants whch are all related to one repar provder (shown by the brght red crcle). There are also a seres of other connectons that are outsde of the man set of connectons n the bottom left corner. It may be legtmate that some of these connectons are there, as certan adjusters may be famlar wth certan contractors, or certan provders are preferred for certan types of clams. It s at ths pont of the analyss that an experenced adjuster should revew the results to ensure that there are no suspcous connectons. Examples of thngs to look for would nclude hstorcal experence wth the contractor or past nstances of known fraud. Identfcaton of Suspcous Clams The development of predctve models for referrals and actonable clams s mportant because t ensures that the company s protectng tself aganst known and dentfed fraud. However, ths s a laggng ndcator, because n order for certan types of fraud to be pcked up by these predctve models, t needs to have been dentfed hstorcally. Heren les the problem wth usng a tradtonal predctve analytcs structure, the target s not complete. Ths s due to the fact that all fraud that has been present n clams hstorcally has not been dentfed. Also, new fraud adapts and wll not necessarly be pcked up by the hstorcal models. An approach that can be used to dentfy suspcous clams wthout a hstorcal target s the use of clusterng analyses. In a clusterng analyss, clams are categorzed nto groups based on ther characterstcs. The more smlar two clams are, the more lkely they are to be grouped together. The more dssmlar the clams, the more lkely they are to be classfed nto separate groups. Ths approach yelds three ways to dentfy suspcous clams. The frst approach s to revew the overall cluster statstcs to determne f the cluster as a whole s suspcous. Ths s done by lookng at how far the cluster s from other clusters, measured by lookng at the dstance to the nearest cluster. If t s far away from other clusters, as a 10

11 whole t contans clams that can be consdered outlers. Upon further nvestgaton of the clams, t can be determned f the cluster descrpton appears to descrbe clams that are suspcous. The table below shows the results of a cluster analyss on homeowner content clams. The dstance to the nearest cluster s charted, wth the clusters sorted by decreasng dstances. Whle fraud s a problem that costs nsurance companes mllons of dollars every year, the majorty of clams any partcular company handles wll be almost or entrely legtmate. For ths reason, the clams that are dfferent or the outlers represent the best place to begn to nvestgate for suspcous actvty. As can be seen from the chart above, the outler clusters (those wth the hghest dstance to nearest cluster the left sde of the x-axs) can be dentfed and nvestgated further to determne f they represent suspcous actvtes. Ths cumulatve dstrbuton can also be used to dentfy the most suspcous clams and prortze them for further nvestgaton. Potentally suspcous clusters can also be dentfed by lookng at the root mean square error of each cluster. The root mean square error s a measure of how varable the clams are wthn a gven cluster. Ths can dentfy suspcous clams because the hgher the root mean square error, the more dssmlar the clams are, and the fact that a group of more dssmlar clams end up n the same cluster may show evdence that they do not ft well anywhere and therefore mght be suspcous. If the clams are dfferent from each other yet wound up n a cluster together, they can be nvestgated as outlers. The next approach to dentfyng suspcous clams wthn the clusterng analyss s to dentfy clams that are outlers relatve to the other clams n ts cluster. The cluster as a whole may seem reasonable, but there can be a number of clams wthn that cluster that are sgnfcantly dfferent than the typcal clam n that cluster. Ths wll help dentfy clams that mght not seem out of lne as a whole, but based on select characterstcs, are dfferent than clams wth smaller characterstcs. For each clam n a cluster, the dstance of the clam from the mean of the cluster s calculated. Clams wth a hgher dstance from the cluster mean are most dssmlar from the average clam n the cluster, yet do not ft better nto any other cluster. In order to standardze ths measure, the dstance from the mean from the cluster analyss can be used as the target n a lnear regresson model, and a scorecard was developed for a predcted dstance from the mean based on the characterstcs of the clam. Once a predcted dstance s calculated, ths dstance can be translated nto a percentle based on the dstrbuton of the clam dstances from means. 11

12 The chart below shows the average dstance from the cluster mean for clams n a homeowner analyss whch were handled by a publc adjuster nstead of a company adjuster Publc Adjuster Theft Fre Water Weather Other As can be seen n the chart, when a publc adjuster s nvolved n a theft clam, the dstance from the mean of the cluster s sgnfcantly hgher for theft clams, and also for the clams for all the other causes of loss combned (Other). For fre, water, and weather clams, the presence of a publc adjuster s not a sgnfcant factor. Those clusterng approaches are an automated way to put a lst of clams nto order for revew, allowng a company to prortze ts resources. Combnaton of Analytcs and Adjuster Experence Adjuster experence and analytcs can be combned usng a procedure called PRIDIT, whch s the Prncpal Components Analyss of RIDIT scores. The PRIDIT procedure s descrbed below, and more detal can be found n the referenced paper by Brocket et al 6. PRIDIT s a mathematcal technque usng a pror classfcaton of dataset characterstcs to score a dataset when there s no defntve tranng data avalable. The frst step n the PRIDIT analyss s to comple a seres of unvarate exhbts of suspected predctor varables. Note that wthout a target, the unvarate need only contan an exposure amount for each level, whch s clam count. Next, each level of the varable was ranked from 1 to n, wth 1 beng the most lkely characterstc to be fraudulent (ths s where the adjuster experence s ncorporated). Each level should have a rankng, although multple levels may be ranked equally. Each level s rank along wth the exposure dstrbuton s used to calculate a seres of Betas for the varable. A level s Beta s calculated by takng the percentage of total exposure whch has a lower (more fraudulent) rankng and subtractng the percentage of total exposure whch has a hgher (less fraudulent) rankng. An example of the Beta calculaton s shown below. 6 Brocket, Patrck L., Rchard A. Derrg, Lnda L. Golden, Arnold Levne, and Mark Alpert. Fraud Classfcaton Usng Prncpal Component Analyss of RIDITs. Journal of Rsk and Insurance, 69:3, September,

13 Factor Level PRIDIT Rank (lower = more fraud) Publc Adjuster Clam Count Dstrbuton Beta The resultng Betas fall between -1 and 1. Ths standardzaton allows varables wth varyng numbers of levels to be ncluded wthout skewng the analyss. Betas close to negatve one can be nterpreted as belongng to a characterstc more lkely to be fraudulent as well as a characterstc that s less prevalent n the dataset, whch n and of tself s a common red flag n fraud detecton. The ndvdual Betas are then tabulated across varables to produce an overall record Beta. A prncpal components analyss s run n SAS on the ndvdual clam characterstc Betas n the dataset, and the frst prncpal component s used to standardze these tabulated betas across the dataset and also dentfy those betas havng the largest nfluence on the fraud calculaton. Varables wth an absolute value of the weghts whch are close to one can be nterpreted as drvng or havng the most nfluence on the PRIDIT fraud score. Takng these varable weghts, we return to the calculaton of the overall record Beta n order to obtan the PRIDIT Score. Instead of smply summng the ndvdual varable Betas as was done to obtan the overall record Beta, the PRIDIT Score requres the ndvdual varable Beta to be multpled by ts respectve varable weght. The result of ths weghtng s the record s PRIDIT Score. PRIDIT Scores are not predctons. By themselves, a PRIDIT Score can not dvulge any nformaton about a record s fraudulent probablty. However, they do provde a way of rankng the clams. Wthn a dataset, the records wth the lowest PRIDIT Scores should be vewed as the most lkely to be fraudulent. A company can apply the results of ths analyss by drectng ther lmted nvestgatve resources towards clams wth the lowest PRIDIT Scores. Snce the PRIDIT analyss s based on suspcon rankngs by experenced adjusters, the results can be used as an ndependent valdaton of the cluster analyss. To do ths, we separated the clams nto ten decles based on predcted suspcon for both the cluster approach and the PRIDIT approach. These decles were then compared to determne how consstent the two approaches were. The results are shown n the chart below. 13

14 As can be seen from the chart, about 64% of the clams are wthn 2 decles of each other when comparng the cluster decle to the PRIDIT decle. So a majorty of the clams are showng consstent results between the two approaches. However, there are some some clams at ether end of the scale that are not consstent. Agan, these may be suspcous clams that may not have been dentfed hstorcally by adjusters, but because of ther characterstcs are beng flagged as potentally suspcous. APPLICATIONS Ultmately, the analytcs results can be provded to clam adjusters as another tool to assst n dentfyng suspcous clams. Based on each of the approaches descrbed here, scores can be developed, along wth reason codes that dentfy why a clam s scored at the level t s. An example of ths scorecard s shown below. The detals of the clam are shown, along wth the score and the reason codes. Clam Detals Arbtraton 3 Accdent Date 10/18/1999 Report Lag 3 days Report Date 10/21/1999 Days Open 932 Coverage Bodly Injury Lawsut Sut Fled State 46 Accdent Locaton Small Town Injury Severty No Informaton Avalable Clamant Age 46 14

15 Fraud Model Scores Score SIU Referral 53 Indcator Past Identfed Fruad 36 Clam Anomaly 13 Composte 34 Fraud Model Reason Codes 1 Delayed Reportng 2 Accdent n Small Town 3 4 CONCLUSION Insurance clam fraud s a sgnfcant problem, and the focus on clam fraud has ncreased as well. Whle nsurers have developed effectve fraud departments, the realty s that not all fraud s dentfed by companes, and undentfed fraudulent clams are costng the nsurance ndustry bllons of dollars. Predctve analytcs can provde ncreased consstency n the dentfcaton of suspcous clams, help companes uncover new types of fraudulent clams, and provde companes wth a way to prortze clams for nvestgaton purposes. By combnng transparent statstcal technques wth a company s own nternal expertse regardng ther busness, many companes can take sgnfcant steps to detectng and preventng fraudulent clams. In today s envronment, even a small mprovement can have a bg mpact on a company s bottom lne. SAS and all other SAS Insttute Inc. product or servce names are regstered trademarks or trademarks of SAS Insttute Inc. n the USA and other countres. ndcates USA regstraton. Other brand and product names are trademarks of ther respectve companes. 15

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