s.v.p.m’s coe malegaon(bk). department of computer engg....department of computer engg....

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HMM : AN EFFICIENT CREDIT CARDFRAUD DETECTION.

Akshaya Tupe, Tanuja Shinde , Dipali Taware.S.V.P.M’s COE Malegaon(Bk).

Department Of Computer Engg.

Abstract—Due to a rapid advancement in the elec-tronic commerce technology, the use of credit cards hasdramatically increased. As credit card becomes the mostpopular mode of payment for both online as well as regularpurchase, cases of fraud associated with it are also rising.In this paper, we model the sequence of operations incredit card transaction processing using a Hidden MarkovModel (HMM) and show how it can be used for thedetection of frauds. An HMM is initially trained with thenormal behavior of a cardholder. If an incoming creditcard transaction is not accepted by the trained HMMwith sufficiently high probability, it is considered to befraudulent. At the same time we will alert to cardholderabout Fraud via SMS and E-mail.

Index Terms—Internet, online shopping, credit card, e-commerce security, fraud detection, Hidden Markov Model.

I. INTRODUCTION

The popularity of online shopping is growing dayby day. According to an ACNielsen study conductedin 2005, one-tenth of the worlds population isshopping online and credit card is the most popularmode of payment (59 percent). As the number ofcredit card users rises world-wide, the opportunitiesfor attackers to steal credit card details and, subse-quently, commit fraud are also increasing. The onlyway to detect this kind of fraud is to analyze thespending patterns on every card and to figure outany inconsistency with respect to the usual spendingpatterns. Fraud detection based on the analysis ofexisting purchase data of cardholder is a promisingway to reduce the rate of successful credit cardfrauds. As the number of credit card users risesworld-wide, the opportunities for attackers to stealcredit card details and, subsequently, commit fraudare also increasing. The only way to detect thiskind of fraud is to analyze the spending patternson every card and to figure out any inconsistency

with respect to the usual spending patterns. Frauddetection based on the analysis of existing purchasedata of cardholder is a promising way to reduce therate of successful credit card frauds.

II. COMPARISON BETWEEN EXISTING SYSTEMAND PROPOSED SYSTEM

A. Existing System

1) Credit Card Transaction Fraud Detection byusing Hidden Markov Model

Nitin Mishra, Ranjit Kumarand and ShishirKumar Shandilya proposed a HMM (HiddenMarkov Model) based fraud detectionsystem for credit card fraud detection. Themethod works on the statistical behaviorof users transactions. Since the originaltransactions are not available due to privacypolicies of bank we used here syntheticallygenerated data for a credit card user, andthen HMM model is trained .The systemhas been tested on a Pentium 4 PC with2 GB of RAM; the test program is codedin MATLAB 7.5. In that paper, the HMMbased method of detection outliers is usedfor set up a detection model, which couldmine fraud transactions as outliers therebyprovide decision support to prevent fraudsand to control risks. Many outlier detectionalgorithms such as base on statistics anddistance are gain good application. Thisshows the maximum accuracy up to 83

2) A Web Services-Based Collaborative Schemefor Credit Card Fraud Detection.

International Journal of Scientific & Engineering Research, Volume 4, Issue 12, December-2013 ISSN 2229-5518

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In the web services-based collaborativedetection scheme, participant banks playsas service consumers, while Fraud PatternsMining Service Center (FPMSC) serves asthe service provider.To achieve data exchangeacross heterogeneous applications of banks,participant banks must obey uniform dataformats for validating the exchanged data.FPMSC publishes a WSDL file that describesthe implementation and interface specificationof its provided service.Banks must obey theregulations in the WSDL file, so that theycan know what data should be sent andwhat patterns will be replied, and understandhow to access the service.In the WSDLfile, the input message of provided serviceis defined as fraud transactions sent frombanks, while the output message is defined asfraud patterns replied to banks. Participantbanks must transform their individual fraudtransactions stored in legacy formats to anXML document that must pass throughthe validation by Patterns-schema accessthe collaborative fraud patterns miningservice provided by FPMSC.The valid XMLdocument is enveloped in SOAP Envelopewithin SOAP Message.The SOAP Messagecan be sent to FPMSC via popular protocolssuch as HTTP, SMTP, and MIME. FPMSCextracts fraud patterns from the integratedfraud transactions using Fraud PatternsMining (FPM) algorithm.When receivingthe SOAP message sent from FPMSC,participant banks can interpret the contentsof PMML document with SOAP messagebased on pattern schema and retrieve fraudpatterns.Through those fraud patterns, bankscan enhance their original fraud detectionsystems to avoid suffering fraud attacks.

3) Problem Reduction In Online PaymentSystem Using Hybrid Model

To solve the problem of fixed fraud data herepresents a new model which does not requiredfixed fraud data but can detect the fraudby cardholder spending behavior mobileactivities. This model uses the combination oftwo techniques ,one is based on the spending

profile i.e. HMM other is based on themobile implicit authentication system.

4) Credit Card Fraud Detection Using DecisionTree for Tracing E-mail and IP.

Decision Tree have become one of themost powerful and popular approaches inknowledge discovery and Data Mining, thescience and technology of exploring largeand complex of data in order to discoveruseful patterns. Decision Tree was originallyimplemented in decision theory and statisticor highly effective tool in other areas suchas Data Mining, Text Mining, Informationextraction and Machine learning. DecisionTree is a method commonly used in DataMining. Decision Tree is a classifier in theform of tree Structure.Fundamental algorithmused for it is Hunts Algorithm. DecisionTree algorithm is a data mining inductiontechniques that recursively partitions adata set of records using depth-first greedyapproach (Hunts et al, 1966) or breadth-firstapproach (Shafer et al, 1996) until all thedata items belong to a particular class. Adecision tree structure is made of root,internal and leaf nodes. The tree Structure isused in classifying unknown data records. Ateach internal node of the tree, a decision ofbest split is made using impurity measures(Quinlan, 1993). The tree leaves are made upof the class labels which the data items havebeen group.Decision Tree using Credit Card FraudDetection: In credit card fraud detectionusing Decision tree the Transaction amountdivided into two levels such as High andLow. We can find the location of thecustomer through IP address. IP addresstraces the transaction location of thecustomer/merchants. Email Tracing can bedivided in to two types:

a) Merchant Tracing Customer Email Ad-dress

b) Customer Tracing Merchants Email Ad-dress.


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B. Demerits Of Existing SystemIn existing system of credit card fraud detection

system fraudulent transaction is detected after trans-action is done.It is difficult to find out fraudulentand regarding losses will be barred by issuingauthorities.

III. PROPOSED MODEL

HMM (Hidden Markov Model) A HMM is afinite set of states; each state is linked with aprobability distribution. Transaction among thesestates is governed by a set of probabilities calledtransition probabilities. In this prediction process,HMM considers mainly three price value rangessuch as,

1) Low (l),2) Medium(m),3) High(h).

Model DescriptionThe whole system is divided into three differentmodels, these models are

1) Registration And TrainingIt comprises with many steps, first is to lo-gin into a particular site to purchase goodsor services, then choose an item and nextstep is to go to payment mode where creditcard information will be required. After fillingall these information, now the page will bedirected to proposed fraud detection systemwhich will be installed at banks server or mer-chant site.k-means is an unsupervised learningalgorithm for finding out the centroids of gen-erated clusters.The Baum-Welch algorithm isused for calculating the HMM transition stateand probability.Forward Backward procedureis used in training phase of HMM. i.e this twoprocedures are used for training the HMM.

2) Fraud Detection SystemAll the information about credit card (LikeCredit card number, credit card CVV number,credit card Expiry month and year, name oncredit card etc.) will be checked with creditcard database. If User entered database is cor-rect then it will ask Personal Identity number(PIN). After matching of Personal Identitynumber (PIN) with database and account bal-ance of users credit card is more than thepurchase amount, the fraud checking modulewill be activated. The verification of all data

will be checked before the first page load ofcredit card fraud detection system.If user credit card has less than 10 transactionsthen it will directly ask to provide personal in-formation to do the transaction. Once databaseof 10 transactions will be developed, thenfraud detection system will start to work.By using this observation, determine usersspending profile. The purchase amount willbe checked with spending profile of user. Bytransition probabilistic calculation based onHMM, it concludes whether the transaction isreal or fraud. If transaction may be concludedas fraudulent transaction then user must entersecurity information. This information is re-lated with credit card (like account number,security question and answer which are pro-vided at the time of registration). If transactionwill not be fraudulent then it will direct togive permission for transaction. If the detectedtransaction is fraudulent then the Securityinformation form will arise. It has a set ofquestion where the user has to answer themcorrectly to do the transaction. These formshave information such as personal, profes-sional, address; dates of birth, etc are availablein the database. If user entered informationwill be matched with database information,then transaction will be done securely. Andelse user transaction will be terminated andtransferred to online shopping website.HMMreceives cardholder transaction sequence anddecide that transaction is fraud or real. Theprocess flow of proposed module is shown infollowing Figure.

3) Alerting User Regarding Fraud TransactionIf HMM concluded that the transaction isfraud then the SMS is send to the credit cardholder about fraud detected. The SMS is sendto mobile phone via SMTP gateway server onthe internet.

Merits of Proposed System:

1) It gives better accuracy than other system.2) The system can scalable for handling large

volumes of transaction.

HMM Can be Mathematically defined as follows:


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Fig. 1. Proposed Model Of Credit Card Fraud Detection

1) N is number of states in the model setof states is s = s1, s2, s3...sN , Wheres1, s2, s3, ..., sN are individual states at anytime t is qt.

2) M is number of distinct observation sym-bols. Observation symbols correspondingto physical output of system being mod-eled. We denote set of observation symbolsV=v1, v2, v3, ..., vM . Where v1, v2, v3...vMare individual observation symbols.

3) State transition probability matrix A=[aij] .Where aij is transition probability from statei and j.aij = P (qt+1 = Sj|qt = Si) ,

1 ≤ i ≤ N, 1 ≤ j ≤ N ; t = 1, 2, ... (1)

4) The observation symbol probability matrixB=[bj (k)]. Where bj (k) is the probabilitydistribution of observation symbol k at statej.

bj (k) = P (Vk|Sj) , 1 ≤ j ≤ N, 1 ≤ k ≤M(2)

5) Initial state distribution π = [πi] where

πi = P (q1 = Si) , 1 ≤ i ≤ N (3)

6) The observation sequence O =O1, O2, O3, ...., OR, where each observationsequence Ot one of the observation symbolsfrom V, and R is the number of observationsin the sequence.

1) Generating Observation SymbolsEquation which calculates Spending profile

using K-means clustering algorithm,SP = MAX (Pi) Where Pi percentageof number of transaction those belongs tocluster i, 1 ≤ i ≤M .

2) HMM TrainingBaum-Welch algorithm is as follows:particular observation sequence isO1, O2, O3....OT .initialization: set λ = (A,B, π) with randominitial conditions. The algorithm updates theparameter of λ iteratively until convergence,following the procedure below

The forward procedure: We define:αi (t) =P (O1, O2, O3....Ot, St = i|λ), which is theprobability of seeing the partial sequenceO1, O2, O3....Ot and ending up in state i attime t. We can efficiently calculate αi (t)recursively as:

αi (t) = πibi(O1) (4)

αj (t+ 1) = bi (Ot + 1)N∑i=1

αi (t) .aij (5)

The backward procedure: This is theprobability of the ending partial sequenceO1, O2, O3....OT given that we started at statei, at time t.We can efficiently calculate βi (t)as:

βi (t) = 1 (6)

βi (t) =N∑j=1

βj (t+ 1) aijbj (Ot+1) (7)

Using α and β, We can calculate the followingvariables:

γi (t) = P (St = i|O, λ) = αi (t) βi (t)∑Nj=1 αj (t) βj (t)

(8)

ξij (t) = P (St = i, St+1 = j|O,

λ =αi (t) aijβj (t+ 1) bj (Ot+1)∑N

i=1

∑Nj=1 αi (t) aijβj (t+ 1) bj (Ot+1)

(9)

having γ and ε, one can define update rulesas follows:

πi = γi (1) (10)


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aij =

∑T−1t=1 εij (t)∑T−1t=1 γi (t)

(11)

bi (t) =

∑t = 1T δOt ..okγi (t)∑T

t=1 γi (t)(12)

Using the updated values of A,B and π, anew iteration is performed until convergence.

3) Fraud Detectionlet initial sequence of observation symbol oflength R up to time t is O1, O2, O3, ..., OR. Wecalculate the probability of acceptance of thissequence by HMM, let α1 be the probabilityof acceptance of this sequence by HMM, letα1 be probability of acceptance.α1 = P (O1, O2, O3, ..., OR|λ) .At time t+1 sequence is O2, O3, O4, ..., OR+1

, let α2 be the probability of acceptance of thissequenceα2 = P (O2, O3, O4, ..., OR+1|λ).Let 4α = α1 − α2

If4α > 0, it means new sequence is acceptedby an HMM with low probability, and itcould be a fraud. The new added transactionis determined to be fraudulent if percentagechange in probability is above threshold, thatisThreshold ≤ 4α|α1

If OR+1 is malicious, the issuing bank doesnot approved the transaction, and the FDSdiscards the symbol. Otherwise,OR+1 is addedin the sequence permanently, and the newsequence is used as the base sequence for de-termining the validity of the next transaction

IV. CONCLUSION

We have proposed an application of HMM inCredit card fraud detection in different steps . Creditcard transaction processing are represented as theunderlying stochastic process of an HMM. we haveused the ranges of transaction amount as the ob-servation symbols.where as the type of item havebeen considered to be states of the HMM. wehave suggested a method for finding the spendingprofile of card holder,as well as deciding the valuesof observation symbol. HMM can detect whetheran incoming transaction is fraudulent or not. Thesystem is also scalable for handing large volume oftransaction.

REFERENCES

[1] Siva Parvati.Nelluri, Shaikh. Nagul , Dr .M.Kishorekumar”Credit Card Fraud Detection Using Hidden Markov Model”.

[2] SHAILESH S. DHOK. ”Credit Card Fraud Detection UsingHidden Markov Model.”

[3] Avinash Ingole, Dr. R. C. Thool. ”Credit Card Fraud DetectionUsing Hidden Markov Model and Its Performance.”

[4] Dr R.DHANAPAL , GAYATHIRI.P ”Credit Card Fraud Detec-tion Using Decision Tree and for Tracing E-mail And IP. AbhinavSrivastava, Amlan Kundu, Shamik Sural, Senior Member, IEEE,and Arun K. Majumdar, Senior Member, IEEE IEEE Transactionfor ”Credit Card Fraud Detection Using Hidden Markov Model”

[5] Sonali N.Jadhav,Kiran Bhandari. ”Anomaly Detection UsingHidden Markov Model”

[6] P.Amarnath Raddy,K.Srinivas. ”Credit card Fraud Detection andalerting Using Hidden Mark over Model SMS Gateway.”


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s.v.p.m’s coe malegaon(bk). department of computer engg....department of computer engg....

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