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Volume : 2 | Issue : 3 | March 2013 ISSN - 2250-1991 Research Paper Engineering Credit Card Fraud Detection Based on User Profile and Previous Transaction * Nitika Kadam, **Suryakant Soni, ***Devendra Puntambekar, ****Rahul Kaul * Computer Science Engineering, Mandsaur Institute of Technology, Indore ** Computer Science Engineering, Mandsaur Institute of Technology ,Indore *** Information Technology, Mandsaur Institute of Technology, Indore **** Computer Science Engineering, BM College,Indore ABSTRACT This paper reports on the findings of a research project that had the objective to prevent the customer from the fraud of credit card. In today scenario we see that most of the people uses transaction through credit card either virtually or physically. The most accepted payment mode is credit card for both online and offline in today’s world, it provides cashless shopping at every shop in all countries. It will be the most convenient way to do online shopping, paying bills etc. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In this paper, the concept of data mining and Hidden Markov Model is used to detect the credit card fraud during transactions. Hidden Markov Model is the statistical tools for engineer and scientists to solve various problems. An Hidden Markov Model is initially trained with the normal behavior of a customer who hold the credit card. With the concept of data mining, previous information about the transaction is extracted then on the basis of extracted information, Hidden Markov Model consider whether the current transaction is either fraudlent or genuine. This paper shows how advanced data mining techniques and HMM can be combined successfully to detect the credit card fraud Keywords: Credit Card , Internet,Data Mining, HMM, Online Shopping, Offline Shopping INTRODUCTION In day to day life credit cards are used for purchasing goods and services. The most accepted payment mode is credit card for both online and offline. For online transaction it uses virtual card and for offline transaction it uses physical card. In today’s world, credit card provides cashless shopping at every shop.It will be the most convenient way to do online shopping, paying bills etc. Therefore, risks of fraud transaction using credit card has also been increasing.To purchase goods in physical transaction,Credit cards will insert into payment machine at merchant shop. In a physical-card based purchase, the cardholder presents his card physically to a merchant for making a payment. To carry out fraudulent transactions in this kind of purchase, an attacker has to steal the credit card. In online payment mode, attackers need only little information for doing fraudulent transaction. In this purchase method ,transactions will be done through Internet or telephone. To commit fraud in these types of purchases, an attacker simply needs to know the card details.The only way to detect this kind of fraud is to analyze the spending patterns and to figure out any inconsistency. HIDDEN MARKOV MODEL In a Hidden Markov model, the state is not directly visible, but output, dependent on the state, is visible. Each state has a probability distribution over the possible output tokens. Therefore the sequence of tokens generated by an HMM gives some information about the sequence of states.A Hidden Markov Model is a finite set of states; each state is linked with a probability distribution. Transitions among these states are governed by a set of probabilities called transition probabilities. Credit card fraud detection based on Hidden Markov Model, which does not require fraud signatures and still it is capable to detect frauds just by bearing in mind a cardhold- er’s spending habit. With the help of Hidden Markov Model we will be able to find out the fraudulent transaction by using spending profiles of user. This works on the user spending profiles which can be divided into major three categories: · Lower profile · Middle profile · Higher profile It keeps record of spending profile of the card holder by both way, either offline or online. Hence, Hidden Markov Model is a perfect solution for addressing detection of fraud transaction through credit card. DATA MINING Data Mining is used to detect fraud for its effectiveness. Data mining is a process that uses a variety of data analysis tools to discover patterns and relationships in data that may be used to make a valid prediction. The six basic steps of data mining process are defining the problem, preparing data, exploring data, building models, exploring and validating models, deploying and updating models. Data mining is a field at the intersection of computer science and statistics, is the process that attempts to discover patterns in large sets. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data preprocessing, model and inference considerations,interestingness,met rics,complexity considerations,post-processing of discovered structures, visualization, and online updating. TECHNIQUE AND ALGORITHM USED Technique that is used for the detection of credit card fraud based on the concept of data mining is Knowledge Data Discovery (KDD). PARIPEX - INDIAN JOURNAL OF RESEARCH X 1 Volume : 2 | Issue : 3 | March 2013 KDD: Many people treat data mining as a synonym.for popularly used term KDD. The process involves in KDD are: · Data Cleaning · Data Integration · Data Selection · Data Transformation · Data Mi ning · Pattern Evaluation · Knowledge Presentation ISSN - 2250-1991 then it will direct to give permission for transaction. If the detected transaction is fraudulent then the Security information form will arise. It has a set of question where the user has to answer them correctly to do the transaction. These forms have information such as personal, professional, address; dates of birth, etc are available in the database. If user entered information will be matched with database information, then transaction will be done securely, else user transaction will be terminated. RESULT In this section, it is shown that fraud detection will be checked on previous transactions and also calculate percentage of each spending profile (low, middle and high) based on total number of transactions. In Table, list of all transactions are shown: No. of Transaction 1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th An algorithm called as HMM is used for the detection of credit card fraud.This algorithm primarily focus on three categories: · Lower Profile · Middle Profile · High Profile This algorithm performs prediction analysis for the fraud detection. Prediction symbol for this model is V { l, m,h},where l stands for lower profile,m stands for middle profile and h stands for higher profile.E.g. If card holder perform a transaction as Rs 2000 and card holders profile groups as l (low) = (Rs 0,Rs 1500], m (middle) = (Rs 1500, Rs 5000], and h (high) = (Rs 5000, up to credit card limit], then transaction which card holder want to do will come in middle profile group.So the corresponding profile group or symbol is M and V (2) will be used.Credit card holder purchase various product with different amount in various period of time. It uses the deviation in a purchasing amount of latest 5 transaction sequence (and adding one new transaction in that sequence) which is one of the possibilities related to the probability calculation.If model does not have data of last 5 transactions in the initial stage, in that case, model will ask to the cardholder to feed basic information during transaction about the cardholder such as address, annual income,date of birth,email id etc.On the basis of this information HMM model obtained relative data of transaction for further verification on spending profile of cardholder. Amount 140 125 15 5 10 125 15 120 10 280 No. of transaction 11th 12th 13th 14th 15th 16th 17th 18th 19th 20th Amount 210 550 800 110 135 118 20 148 141 6 The most recent transaction is placed at the first position and correspondingly first transaction is placed at the last position in the table .The pattern of spending profile of the card holder is shown in below Figure based on all transactions done. The percentage calculation of each spending profile (low, medium and high) of the card holder based on price distribution range as mentioned earlier is shown in below Figure VERIFICATION OF FRAUD TRANSACTION: All the information about credit card such as Credit card number, CVV number, credit card Expiry month and year, name of credit card holder etc will be checked with credit card database. If cardholder entered database is correct then it will ask Personal Identity number (PIN). After matching of Personal Identity number (PIN) with database and account balance of user’s credit card is more than the purchase amount, the fraud checking module will be activated. If user credit card has less than 5 transactions then it will directly ask to provide personal information to do the transaction. Once database of 5 transactions will be developed, then fraud detection system will start to work. By using this observation, determine users spending profile. The purchase amount will be checked with spending profile of user. By transition probabilistic calculation based on HMM, it concludes whether the transaction is real or fraud. If transaction may be concluded as fraudulent transaction then user must enter security information. This information is related with credit card (like account number, security question and answer which are provided at the time of registration). If transaction will not be fraudulent 2 X PARIPEX - INDIAN JOURNAL OF RESEARCH CONCLUSION In this paper it has been discussed that how Hidden Markov Model will facilitate to stop fraudulent transaction through credit card. With the help of HMM, based on three categories such as low profile, middle profile, high profile and on the previous transactions of credit card holder our proposed model determine whether the current transaction is genuine or fraud. If fraud transaction occurs then it terminate the current transaction else continue with the transaction. Our proposed model suggest that if the previous transaction of a card holder is not present then information related with credit card like account number, security question and answer must provided by the cardholder. The Hidden Markov Model makes the processing of detection very easy and tries to remove the complexity. Volume : 2 | Issue : 3 | March 2013 ISSN - 2250-1991 REFERENCES 1. Ghosh, S., and Reilly, D.L., 1994. Credit Card Fraud Detection with a Neural-Network, 27th Hawaii International l Conference on Information Systems, vol. 3 (2003), pp. 621- 630. | 2. Aleskerov, E., Freisleben, B., and Rao, B., 1997. CARDWATCH: A Neural Network Based Database Mining System for Credit Card Fraud Detection, Proceedings of IEEE/IAFE: Computational Intelligence for Financial Eng.(1997), pp. 220-226. | 3. Syeda, M., Zhang, Y. Q., and Pan, Y., 2002 Parallel Granular Networks for Fast Credit Card Fraud Detection, Proceedings of IEEE International Conference on Fuzzy Systems, pp. 572-577 (2002). | 4. Data Mining- Concepts And Techniques by Han and Kamber. | 5. Hidden Markov Models, Theory and Applications by Przemyslaw Dymarski | 1. Ghosh, S., and Reilly, D.L., 1994. Credit Card Fraud Detection with a Neural-Network, 27th Hawaii International l Conference on Information Systems, vol. 3 (2003), pp. 621- 630. | 2. Aleskerov, E., Freisleben, B., and Rao, B., 1997. CARDWATCH: A Neural Network Based Database Mining System for Credit Card Fraud Detection, Proceedings of IEEE/IAFE: Computational Intelligence for Financial Eng.(1997), pp. 220-226. | 3. Syeda, M., Zhang, Y. Q., and Pan, Y., 2002 Parallel Granular Networks for Fast Credit Card Fraud Detection, Proceedings of IEEE International Conference on Fuzzy Systems, pp. 572-577 (2002). | 4. Data Mining- Concepts And Techniques by Han and Kamber. | 5. Hidden Markov Models, Theory and Applications by Przemyslaw Dymarski | PARIPEX - INDIAN JOURNAL OF RESEARCH X 3