A discrete choice approach to model credit card fraud

Transcription

1 Manuela Pulna (Italy), Paba Antonello (Italy) A dscrete choce approach to model credt card fraud Abstract Ths paper analyzes the demographc, soco-economc and bankng-specfc determnants that nfluence the rsk of fraud n a portfolo of credt cards. The study s based on a large bankng portfolo and s the frst to analyze the statstcal sgnfcance of the determnants of credt card fraud n Italy. A logt framework s employed that ncorporates cards at a rsk of fraud as the dependent varable and a set of control varables (e.g., gender, locaton, credt lne, number of transactons n euros and n non-euros currency). The emprcal results provde useful ndcators on the factors that are responsble for potental rsk of fraud. Overall, the rsker categores are women, customers resdng n the Center of Italy, those who own a II crcut credt card and secondary owners. Keywords: credt card, fraud, demographc and soco-economc factors, logt modelng. JEL Classfcaton: D12, G21, C35. Introducton The current global recesson s hghlghtng the fraglty of the global bankng and fnance system that s subject to a greater rsk and acts of fraud. There are new challenges n tacklng fraud stemmng from a fast changng nformaton technology envronment, where the nternet has become one of the most mportant channels for the retal sector. Kageyama (29) reports that n the past three years more than 9 companes surveyed at a worldwde level have lost an average of 8.2 bllon dollars a year, a 22% ncrease wth respect to the prevously publshed research. Moreover, the percentage of frms that regstered at least one fraud n 28 has reached 85% that s an 8% ncrease n the prevous year. Whle these fgures hde the motvaton for fraud, the rates of growth are sgnfcant and n a tme of recesson ths rate s more lkely to ncrease as hgher numbers of ndvduals commt fraud (Abbey, 29). In 25, the world s two largest credt card crcuts, Vsa and MasterCard, reported 1.14 bllon dollars of fraud losses that represented a 62.9% ncrease wth respect to In the Unted Kngdom, for example, credt card fraud s one of the fastest growng crmes and n 28, total card fraud losses amounted to more than 69 mllon pounds, of whch 52.5 mllon was attrbuted specfcally to onlne bankng fraud (Assocaton for Payment Clearng Servces, APACS, 29b). Arguably, these hgh amounts can be partally explaned by the hgh volume of transactons and remarkable growth n credt cards ownershp over the past three decades. Vsa (23) calculates a 1% year on year compound growth snce cards were frst ssued. The USA, for nstance, denotes the hghest number of ssued cards (more than 1.5 bllon) and each nhabtant owns on average more than 5 payment nstruments. In Europe, however, the average card holder owns 1.3 cards and the UK Pulna Manuela, Paba Antonello, 21. The authors thank the fnancal support provded by Fondazone Banco d Sardegna (Prot. 111/29.12). confrms ts predomnance wth 22.3% of all EU cards and 2.4 cards per capta (Assofn-Crf-Eursko, 28). Such mportant fraud losses are drvng ncreasng efforts n both the detecton and preventon of fraud and the mplementaton of robust rsk management practces n the credt card ndustry. In ths paper, credt card fraud s defned as the msuse of a card wthout authorzaton or unapproved purchases or the counterfetng of cards (Wells, 27). The motvaton and opportunty behnd credt card fraud are many and vared. Tradtonal types of fraudulent behavor such as dentty theft relate to famly members or people that can easly access ndvdual s mal and personal nformaton and commttng fraud ether by applyng for a card or takng over the exstng account. Dumpster dvng or trashng, where crmnals rad rubbsh bns to search for credt card detals and other senstve nformaton s becomng more wdespread. Lost or stolen credt cards may also be used fraudulently. Skmmng of the magnetc strpe s also stll practced ether usng hghly sophstcated devces embedded n ATM s or POS or usng smple hand held skmmers capable of storng magnetc strpe data. Internet enabled fraud s also growng; phshng attacks contnue to harvest credt card users detals and compromzed computers wth key loggers provde organzed crmnals wth the card detals. As the vast majorty of all credt card transactons are now authorzed and cleared on-lne, hackng nto the e-payment chan to ntercept data can harvest many mllons of card detals. The e-fraud market has grown. Crmnals are now provded wth varous nternet resources to counterfet credt cards, examples are tppng, custom embossng, decodng machnes as well as software such as Credtmaster. A common practce s also that of phshng where fraudulent emals hjackng brand name of banks, credt cards companes, etc. are sent amed at acqurng trckly fnancal data, account usernames and passwords. Organzed crme s normally composed of professonal crmnals that are settng cardng 79

2 forums where t s possble to buy wde-scale global stolen personal and fnancal nformaton. Ths practce that leads to the unauthorzed use of senstve nformaton to purchase goods and servces often nvolves thousands and even mllons of vctms (Perett, 28). Indeed, credt card fraud s subject to technologcal enhancement and t s n a contnuous evoluton. However, due to the lack of statstcal nformaton on fraud MasterCard, for example, s the only nternatonal crcut that provdes statstcal nformaton on credt card fraud few mcroeconomcs studes are avalable n ths feld. The purpose of ths paper s to analyze the demographc, soco-economc and bankng-specfc determnants that nfluence the rsk of a credt card fraud. The emprcal nvestgaton s based on a unque dataset contanng approxmately 32, observatons from a recent credt card portfolo ssued n Italy. In 24, MasterCard reported that the percentage of fraud n all European countres was approxmately.7% of card holder expendture whle n Italy ths fgure was.5% (Affar & Fnanza, 29). The Assocaton for Payment Clearng Servces (APACS, 29a) reports that at a worldwde level Italy s one of the top fve countres to have seen an ncrease n the use of fraudulent UK credt cards. Fraud on UK-ssued cards beng used n Italy has ncreased by 72.9% snce 25, to 8.3 mllon n 28. For the portfolo of cards under study, the emprcal analyss s performed on the rsk of fraud across three product categores that are classc, gold and revolvng. The econometrc approach s based on a logt framework, where the probablty that a gven credt card experences a fraud s estmated. The dchotomous dependent varable s regressed on a set of explanatory varables (e.g., gender, locaton, outstandng balance, number of transactons n euros and non euros). The emprcal results provde useful ndcators on the factors that are lkely to nfluence fraud events based on the avalable sample. Hence, ths framework offers not only a mcroeconomcs perspectve to analyze the rsk of a fraudulent behavor but most mportantly an approach to help rsk managers n fraud preventon by provdng nsght nto whch factors may lead to fraudulent events. The paper s organzed as follows. In the next secton, a lterature revew on credt cards fraud s provded. In the second secton the methodology employed s hghlghted. In the thrd secton, man emprcal results are provded and concludng remarks are gven n the last secton. 1. A lterature revew on credt card fraud Fraud lterature, that frst appeared n the 198s, treated a wde range of economc actvtes such as nsurance fraud (Donne, 1984; Clarke, 1989; Artís, Ayuso and Gllén, 1999; Caudll, Ayuso and Gullén, 25; Boyer, 27), medcal fraud (Pontell and Ges, 1982; Feldman, 21; Ra, 21) and accountng fraud (Beasley, 1996; Gerety and Lehn, 1997; Sadka, 26; Crutchley et al., 27). Though fraudulent behavor has been analyzed n fnancal markets (partcularly for securtes, bankruptcy and money launderng), very few studes exst on credt card fraud. One of the frst papers that appeared n the law and crmnology lterature on credt card fraud (Camner, 1985) emphaszed the need to allow authortes greater access to ssuer records and to the USA federal government to devote greater resources to the nvestgaton of credt card fraud. Hence, snce the 8 s there has been the belef that credt card fraud was ncreasng at an astoundng rate and losses were borne by customers themselves. It s nterestng to note that n Italy over 2 years after the Camner s appeal, all card ssuers are oblged to report all credt card fraud nto a central database (Decreto, 27). As Bolton and Hand (22) pont out, t s mportant to dstngush between fraud preventon, that s measures to stop fraud occurrng n the frst place and fraud detecton, that s measures to dentfy fraud as quckly as possble, once t has been perpetrated (for a lterature revew, see Delamare, Abdou and Ponton, 29). Very few studes analyze how to prevent fraud occurrng n the frst place. Masuda (1993), for example, reports a successful retal strategy n credt card fraud preventon. There s evdence that program ntatves and an ndustry-wde acton wth the exchange of fraud-related ntellgence data amongst regonal and natonal authortes seem to be the key to reduce credt card fraud. Wllams (27) presents a case study of credt card fraud n Trndad and Tobago, new entry countres nto the credt card market, n whch a preventon actvty can be mproved by ssung specfc laws, educatng and nformng the publc of the varous fraudulent typologes and enhancng the crtcal role of the bankng assocatons n formulatng ad hoc prncples and polces to control ths type of crme. Barker, D Amato and Sherdon (28) descrbe numerous schemes and technques and gve addtonal ones. However, detecton and preventon cannot be always regarded as dstnct actons. To a certan extent t s possble to learn from the past fraud patterns n order to prevent smlar cases occurrng n the future. To ths am, several statstcal technques are avalable such as lnear regressons, classfcaton trees, naïve Bayes, neural networks and self-organzng maps (SOM) that are based on the clusterng capabltes of neural networks (Thomas, Olver and Hand, 25). These technques are amed at dscrmnatng f a new credt card transacton belongs to the genune set or to the fraudulent set. More recently, Quah and Srganesh 8

3 (28) propose a real-tme fraud detecton and present a new approach to analyze card owners behavor for detecton of fraud. In the lterature, a commonly used technque to detect credt fraud s logstc regresson. Such an econometrc tool, together wth the above mentoned technques, s mostly employed wthn the credt scorng process to help nsttutons and organzatons decde whether to ssue credt to consumers who apply for t (Desa, Crook and Overstreet, 1996; Greene, 1998; Thomas, 2; Crook and Banask, 24; Abdu, 29; Sušterš et al., 29). From the present lterature revew, t emerges that emprcal studes are manly concentrated on a pror understandng of whether or not to provde consumers wth a gven bank product (such as a credt card or loan). However, very few papers employ mcroeconomcs bank data to analyze the factors that nfluence the rsk of fraud n a portfolo of credt cards. In a recent study, Hartmann-Wendels, Mählmann and Versen (29) make use of a sample of approxmately 2 thousand observatons, on successful applcants from a German nternet-only bank, to analyze what factors do affect fraud. The man fndng s that fraud rsk s hghly nfluenced by determnants such as gender, cvl status, age, occupaton and urbanzaton. Above all, natonalty matters as foregn customers are tmes more lkely to perpetrate a fraud than natonals. Hence, the present paper ams at expandng ths strand of research, provdng new evdence on the man factors affectng the rsk of fraud wthn the Italan credt card market, through the analyss of an extensve credt card data set at a mcroeconomc level. 2. Methodology 2.1. Defntons and data. In 28, credt cards n Italy represented around 45% of the total card payment system (DataBank, 29). The emprcal model presented n ths study makes use of bankng mcroeconomc data on only three types of credt cards: classc, gold and revolvng. Mass market credt cards, sometmes referred to as classc cards, are well-spread worldwde and have standard characterstcs; relatvely lmted avalablty to spend, POS and ATM functonalty and low or no annual fees. Gold credt cards are generally held by customers that are more lkely to have hgher ncomes and a hgher spendng propensty. Ths typology of card s, n fact, characterzed by hgher jonng and annual fees, hgher spendng lmts and daly cash wthdrawal lmts snce they often provde extra servces such as travel nsurance, roadsde assstance, hotel and car-rental advantages, ar travellng executve prvleges, world wde support n the event of loss or theft of the card, etc. The gold card can be thought as a more sophstcated classc where the addtonal servces are mandated by the nternatonal crcut. A revolvng card s often smlar to the classc, or the gold card, but allows the customer to spread the payment over a seres of bllng cycles for a fee or nterest charges. The owner of the revolvng card, therefore, has the need to borrow or control the way the debt s repad. Ths payment nstrument s more lkely to be nfluenced by economc turmol (Assofn-Crf-Eursko, 29). The emprcal model that follows s tested usng data collected from recent bank card account archves for an Italan based ssuer. The cards were ssued on prncple global crcuts. Approxmately, 32 thousands card postons are consdered. Each observaton can be thought as a photo of each clent that summarzes ther poston. It s mportant to note that ths paper employs data on actual postons, and hence, one can derve a statc analyss on consumers preferences. Classc and gold cards, ssued to clents that hold a current account wth the ssuer, represent 6.1% and 3.4%, respectvely, of the postons. The revolvng cards, 36.5% of the postons, were ssued after a credt scorng procedure appled by the bank. In ths crcumstance, t s possble that the use of ex post data, only wth selected ndvduals, leads to based estmates of the probablty to acqure a gven payment nstrument. Ths ssue s not uncommon n the lterature. Hartmann-Wendels, Mählmann and Versen (29), for example, make use of a sample of approxmately 2 thousands successful applcants from a German nternet-only bank, to analyze what factors affected fraud. Hence, even factors that do not explctly enter the acceptance scorng process, but are at the same tme correlated wth the varables n the fraud equaton may cause based predctons. However, as n ths case, no data on rejected applcatons are avalable. Nevertheless, the fndngs emergng from actual data on successful applcatons can shed lght on understandng potental fraudulent behavor n credt markets. Besdes, from the bank s perspectve understandng determnants of fraud rsk for the exstng customer portfolo s mportant to rsk managers who have to detect, control and prevent credt card fraud. The dependent varable used n the logt model s rsk of fraud where rsk of fraud s defned as a stuaton where there s rsk of actual evdence of unauthorzed use of a credt card or ts detals. These nclude also cards that are reported as stolen that are at rsk of fraud The model specfcaton. The am of the emprcal analyss s to estmate the probablty that a gven type of credt card experences a rsk of fraud. Ths framework s made operatonal by usng a partcular dstrbuton for the dsturbances, that s a logt model wthn a dscrete choce structure (Greene, 23). Formally, a vector of dependent varables s observed Y = (Y 1, Y 2 ). Specfcally, f a 81

4 customer s characterzed at rsk of fraud (ether where the customer s the fraudster or s a vctm of fraud), and hence, a gven credt card s at a fraud rsk, Y takes the value of one; lkewse, f customer belongs to the genune set, and hence, a gven credt card s not subject to rsk of fraud, then Y 2 takes the value of zero. Because coeffcents change from one regme to another, probabltes (P) change leadng to a new ndex (I). In analytcal terms: I, (3) 1 X Y 1 f I, (4) P( Y 1) P( I ) P( 1 X ) 1 X (5) P( X ) P f ( t) dt F( X ), 1 1 X e P( Y 1 X ) ( 1 X ), (6) 1 X 1 e that s the probablty of a fraudulent acton s assumed to be a functon of customers behavor and a set of determnants defned as X,. Followng the emprcal study cted n the lterature revew secton, a descrpton of the set of explanatory varables ncluded nto the model (.e. gender, locaton, crcut, cardholder, outstandng balance, number of transactons n euros and n non-euros and credt lne) s provded n Table 1. Table 1. Lst of control varables Name Defnton Code Gender Ths dchotomous varable takes the value of one f female, zero f male. gen Locaton Crcut Cardholder Outstandng balance Type of transactons Credt lne Dummy varables take nto account the geographcal heterogenetes of Italy. Standard defntons of geographcal zones are employed (ISTAT, 29); North West (nw) North East (ne) Centre (cen) South (sou) Islands (sl) used as the reference group. The locatons of the cardholders are gven by a mappng of ther postal address zp code to geographcal zone usng tables produced by the Insttute Post Offce. Ths dummy varable takes one as a value f the card has been ssued by the frst crcut and zero by the second crcut. Ths dummy varable takes the value of one f the credt card has been ssued for a secondary owner and zero otherwse. It s gven by the sum of all expendture and charges on the card less the amounts pad. For revolvng cards ths translates nto what the customer stll needs to pay off at the end of the bllng cycle whle for classc and gold cards t s what the customer actually pays at the end of the month. Wthn the sample, the maxmum value s 2,62 euros, whle the mnmum value s zero euros. The number of transactons n euros; wthn the sample, the mnmum value s zero, whle the maxmum value s 87 transactons. The number of transactons n a foregn currency rather than euro; wthn the sample, the maxmum value s zero, whle the maxmum value s 58 transactons. Ths s a contnuous varable that accounts for the amount of the spendng lmt of the card. Wthn the sample, the mnmum value s 1, euros, whle the maxmum value s 5, euros. 1 nw ne cen sou sl cr hl ob nteu ntneu cl Table 2 provdes a statstcal descrpton of the dchotomous varables under nvestgaton. The Pearson 2 statstcs tests for the null hypothess that the dstrbuton of fraud case does not dffer across the categores of the varable. Hence, the result s that all the varables are hghly dependent wth the rsk of fraud. Table 2. Descrptve statstcs of dchotomous varables Fraud rsk = % No fraud rsk =1 % Total Frequency 311, , ,231 Gender Male 19, , ,492 Female 12, , ,739 Total 311, , ,231 Pearson 2= (.) Locaton NW 8, ,731 NE 136, , ,57 CEN 13, ,742 SOU 64, , ,993 IS 88, ,195 Total 311, ,231 Pearson 2= (.) Cardholder Prncpal 31, , ,681 Secondary 9, ,55 Total 311, , ,231 Pearson 2= 28.3 (.) Crcut I crcut 24, , ,4 II crcut 16, ,191 Total ,231 Pearson 2= 1.2E+3 (.) Note: p-values are n parentheses. and 1 are the parameters to be estmated; e s the logstc functonal form that allows one to ensure that the estmated choce probabltes le between zero and one; s the cumulatve logstc dstrbuton functon. The logstc regresson s estmated usng maxmum lkelhood. In dscrete choce models, estmated coeffcents cannot be nterpreted n terms of elastcty. Hence, margnal effects, that represent the mpact of a oneunt change n the explanatory varable on the dependent varable, ceters parbus, can be also computed. In analytcal terms, gven the ndex I and the probablty P: I, (7) 1 X P 1X f ( t) dt, (8) then the margnal effect of changes n X on ths probablty s gven by: 82

5 P X f ( 1X ) 1. (9) The margnal effect (ME) for dummy varables s computed by employng the means of all the other varables. Ths gves an accurate measure of elastcty as f the dchotomy varable was a contnuous one (Greene, 23). Regresson results are reported n the second column of Table 3. In the emprcal lterature, more often, odds rato and relatve rsk rato (RRR) are reported (see, for example, Ardç and Yüzererolu, 26). On the one hand, the odds rato for a gven group s gven by the followng expresson: exp a+b*x, (1) where X equals zero when the odds rato refers to the reference group and one otherwse. Odds rato above one, assocated wth postve estmated parameters, ndcates that hgher values of the explanatory varable ncrease the predcted probablty of a gven category compared to the reference category. Coeffcents less than one ndcate the opposte. For dchotomous varables, the RRR expresses the rato of the probablty of choosng one group category over the probablty of choosng the reference group and s gven by the followng expresson: RRR = (group category_odds)/(reference group_odds) (11) see, for example, Hartmann-Wendels et al. (29). 4. Emprcal fndngs The man fndngs from the logt regresson are reported n Table 3. Wth only one excepton, all of the coeffcents are statstcally sgnfcant at the 1% level. The overall statstcs ndcate a well-specfed model: the lkelhood rato test (LR(11)) shows that the coeffcents of the explanatory varables are jontly statstcally sgnfcant; the Hosmer-Lemeshow goodness-of-ft test (that s Pearson 2 ) fals to reject the null hypothess that the dstrbuton fts the data. Table 3. Logt regresson results Coeffcents ME RRR Gender (ref. male) gen.32***.5*** 1.36 Locaton (ref. Islands) nw.6384***.13*** 1.87 ne.5561***.88*** 1.74 cent.8441***.19*** 2.28 sou.3332***.6*** 1.39 Crcut (ref. I crcut) cr ***.138***.36 Ownershp (ref. prncpal) hl.259***.43*** 1.28 Outstandng balance ob ^.2*** -3.52E-6*** 1.*** ^ Type transactons nteu ^.245***.4** 1.25*** ^ ntneu ^.156** Credt lne cl ^.*** 6.28E-7*** 1.*** ^ Number of obs Pseudo R LR 2(11) *** Pearson 2(11) (p-value.998) Log lkelhood Notes: *** and ** ndcate statstcal sgnfcance at the 1% and 5% levels, respectvely; e.g. RRR = (female_odds)/ (male_odds) =.137/.187 = 1.36; ^ odds rato. Consderng the frst dchotomous varable gen (gender), the probablty rato ndcates that women are 1.36 tmes rsker than men. Ths outcome can be nterpreted n a number of ways: women may be more lkely to dscover and report a fraudulent event than men; ther behavor and spendng patterns put the card at a greater rsk; women may commt more fraud than men. As ponted out by Epaynews (29), women are more lkely to commt fraud n mal order, communcatons and loans, whereas men fraudsters are more actve n areas such as asset fnance and nsurance. Locaton turns to be an mportant factor n explanng rsk fraud n credt cards. Customers who are resdents n the Centre of Italy are the rskest group n comparson to the reference one but also to the other geographcal areas. The customers who are resdents n the South tend to show a relatvely lower rsk. The second crcut s affected only 36% by fraud rsk and, hence, the frst crcut has 2.75 tmes more probablty to ncur n a fraud. Ths fndng s of a partcular nterest for bankng nsttutons that should closely consder bankng-specfc features such as the securty standards for a gven crcut. Besdes, ths result may also be explaned by the rsk propensty of the pool of customers nherent to each of the crcuts. As an addtonal outcome, secondary card owners are sgnfcantly more lkely to be at rsk of fraud (Table 3). In general, secondary card owners are close relatves of the prmary owner (e.g., partners, chldren); these ndvduals may be less aware of the varous fraudulent typologes and are more lkely to be at rsk. Overall, the coeffcents of the contnuous varables (ob, nteu, ntneu, cl) are statstcally sgnfcant at the 1% level, wth the only excepton for ntneu. However, the effects on the rsk of fraud are very small, as hghlghted by the margnal effects results. Accordng to the odds rato for nteu and ntneu, the transactons n euros are slghtly rsker than the noneuros transactons. 83

6 Conclusons In the credt market lterature, only a few studes make use of bankng data to analyze the rsk of fraud for a gven set of credt cards (.e. classc, gold and revolvng credt cards). In ths paper, a logt analyss has been conducted to assess whch factors may lead to the rsk of fraud. Fraud has been defned as an unauthorzed use of a credt card, ncludng card detals, and also stolen cards that are potentally at rsk of fraud. To ths am, mcroeconomc data have been employed for more than 3 thousand observatons. Data were obtaned from recent account archves for bankcards ssued throughout Italy. The model has focused on soco-demographc as well as bankng-specfc factors nfluencng the probablty to ncur a rsk of fraud. Margnal effects, odds and relatve rsk rato have been computed. Overall, the coeffcents of the explanatory varables ncluded n the model are statstcally sgnfcant at the 1% level and the model s well-specfed. The results have shown that the rsk of fraud n credt cards s nfluenced by many determnants: gender, locaton, type of crcut, card ownershp, credt lne and number of transactons dvded by currency. Women have been found to be rsker than men ether as fraudsters or vctms of crme. Geographc locaton also matters. The Centre of Italy denotes customers that are lkely to lead to a hgher rsk of fraud for the bank. Overall, the least rsky geographcal zone s the Islands. As a bankng-specfc feature, transactons wthn the second crcut have denoted hgher standards of References 84 securty wth respect to the frst crcut. Furthermore, rsk managers should also ncrease fraud preventon n transactons made by secondary owners who have been found to be 1.28 tmes rsker than prncpal owner. Though the coeffcents of the contnuous varables (ob, nteu, cl) are statstcally sgnfcant, the margnal effects on fraud rsk are qute small. Overall, the transactons n euros are slghtly rsker than the noneuro transactons. The emprcal fndngs show that an ncrease n ether the credt lne or n the amount the clent needs to pay at the end of the bllng cycle should not mply a hgher rsk. Ths paper s a novel example of consumer fnance research on fraud rsk. A dscrete choce model has helped to systematcally analyze the potental fraudulent behavor wthn the credt card market, where customers can be consdered ether as fraudsters or as vctms. The emprcal fndngs offer nsghtful nformaton to rsk managers on the characterstcs and behavor of ther pool of clents and on the potental factors that may lead to fraudulent actons. Bankers can employ actual postons to assess fraud rsk and to dscrmnate customers nto the genune set or the at-rsk set. Customers nformaton and educaton of the varous fraudulent typologes seem a key strategy to prevent credt card fraud, partcularly for women who are often vctms of thefts and secondary owners, as hghlghted n the emprcal nvestgaton. Rsk managers should also closely consder bankng-specfc features such as standards of securty for a gven crcut. 1. Abbey R. Perché n temp d recessone aumentano cas d frode? // Global Fraud Report, Kroll, 29. N 84. p Adbu H.A. An evaluaton of alternatve scorng models n prvate bankng // Journal of Rsk Fnance, 29. N 1 (1). pp Affar & fnanza. Clonazon, truffe e furt d'denttà: furb n agguato. 29. Avalable onlne at downloaded 18 June APACS. 28 fraud fgures announced by APACS. 29a. Avalable onlne at Downloaded 25 August APACS. Fraud the facts. 29b. Avalable onlne at - Downloaded 25 August Ardç O.P., U. Yüzererolu. A multnomal logt model of bank choce: an applcaton to Turkey // Research paper, Boarç Unversty Department of Economc Research Papers, 26 ISS/EC-26-2 p Artís M., M. Ayuso, M. Gullén. Modellng Dfferent Types of Automoble Insurance Fraud Behavour n the Spansh Market // Insurance: Mathematcs and Economcs, N 24. pp Assofn-Crf-Eursko. Osservatoro sulle carte d credto. Numero 6 September 28. Mlano. 9. Barker K.J., J. D'Amato, P. Sherdon., Credt card fraud: awareness and preventon // Journal of Fnancal Crme, 28. N 154. pp Beasley M.S. An Emprcal Analyss of the Relaton between the Board of Drector Composton and Fnancal Statement Fraud // The Accountng Revew, N 71 (4). pp Bolton R.J., D.J. Hand. Statstcal fraud detecton: a revew // Statstcal Scence, 22. N 17 (3). pp Boyer M.M. Resstance to fraud s futle // Journal of Rsk and Insurance, 27. N 74 (2). pp Camner B.F. Credt card fraud: the neglected crme // Journal of crmnal law & crnomology, N 76 (3). pp Caudll S., B. Ayuso, M. Gullén. Fraud detecton usng a multnomal logt model wth mssng nformaton // Journal of Rsk and Insurance, N 72 (4). pp

7 15. Clarke M. Insurance Fraud // Brtsh journal of Crmnology, N 29. pp Crook J., J. Banask. Does reject nference really mprove the performance of applcaton scorng models? // Journal of Bankng & Fnance, 24. N 28 (4). pp Crutchley C.E., M.R.H. Jensen, B. Beverly, B.B. Marshall. Clmate for Scandal: Corporate Envronments that Contrbute to Accountng Fraud // Fnancal Revew, 27. N 42 (1). pp Decreto. Isttuzone d un sstema d prevenzone delle frod sulle carte d pagamento. 3 aprle 27. N Delamare L., H. Adbou, J. Ponton. Credt card fraud and detecton technques: a revew // Banks and Bank Systems, 29. N 4 (2). pp Desa V.C., J.N. Crook, J.G.A. Overstreet. A comparson of neural networks and lnear scorng models n the credt unon envronment // European Journal of Operatonal Research, N 95 (1). pp Donne G. The Effect of Insurance on the Possbltes of Fraud // Geneva Papers on Rsk and Insurance-Issues and Practce, N 9. pp Epaynews.com. Fraud fgures reveal some nterestng gender patterns. Avalable onlne at Downloaded 1 September Feldman R. An Economc Explanaton for Fraud and Abuse n Publc Medcal Care Programs // Journal of Legal Studes, 21. N 3 (2). pp Gerety M., K. Lehn. The Causes and Consequences of Accountng Fraud // Manageral and Decson Economcs, N 18 (7/8). pp Greene W.H. Sample selecton n credt-scorng models // Japan and the World Economy, N 1. pp Greene W.H. Econometrc analyss. - New Jersey: Prentce Hall, Upper Saddle Rver, Hartmann-Wendels T., T. Mählmann, T. Versen. Determnants of banks rsk exposure to new account fraud Evdence from Germany // Journal of Bankng & Fnance, 29. N 33. pp Kageyama J.M. Nuov rsch d frode per le azende gappones: una lezone per tutt // Global Fraud Report, Kroll, 29. N 84. p Masuda B. Credt card fraud preventon. A successful retal strategy n crme preventon studes. Volume 1. Edted by R.V. Clarke, Pontell H.N., G. Ges. Polcng Physcans: Practoner Fraud and Abuse n a Governament Medcal Program // Socal Problems, N 3 (1). pp Quah J.T.S., M. Srganesh. Real-tme credt card fraud detecton usng computatonal ntellgence // Expert systems wth applcatons, 28. N 35. pp Ra A.K. Health Care Fraud and Abuse: A Tale of Behavour Induced by Payment Structure // Legal Studes, 21. N 3 (2). pp Sadka G. The Economc Consequences of Accountng Fraud n Product Markets: Theory and a Case from the U.S. Telecommuncatons Industry WorldCom // Amercan Law and Economcs Revew, 26. N 8 (3). pp Sušterš M., Dušan, J. Zupan. Consumer credt scorng modes wth lmted data // Expert systems wth applcatons: An nternatonal Journal, 29. N 36 (3). pp Thomas L.C.A. survey of credt and behavoural scorng: forecastng fnancal rsk of lendng to consumers // Internatonal Journal of Forecastng, 2. N 16 (2). pp Thomas L.C., R.W. Olver, D.J. Hand. A survey of the ssues n consumer credt modellng research // Journal of Operatonal Research Socety, 25. N 5 (6). pp Wells J.T. Encyclopeda of fraud. // Assocaton of Certfed Fraud Examners, nc., Texas Wllams D.A. Credt card fraud n Trndad and Tobago // Journal of fnancal crme, 27. N 14 (3). pp VISA. Marketng. Hstory of card products and acqurng. // Vsa Internatonal Servce Assocaton, 23. Sectons