A Novel Idea for Credit Card Fraud Detection using Decision Tree

Digitalisation and Big Data Mining in Banking

“…Many researchers worked with transaction data, seeking better approaches to distinguish between patterns from genuine behavior with higher efficiency and accuracy [12][13][14][15][16][17][18][19][20][21][22][23][24][25]. Among these, Wei et al [12] proposed a framework named i-Alertor for major Australian banks; a semi-supervised decision support system named BankSealer was proposed in [14] for an Italian bank; authors in [15] proposed a hybrid DM method to predict network intrusions and detect fraud activities; FraudMiner model that integrated frequent itemset mining was introduced in [16] and verified with the data set from UCSD DM contest 2009; a comparative study [17] addressed the ensemble approach to build classifiers; in terms of a recent advancement in FraudMiner, the authors in [18] introduced the LINGO clustering technique [26] for the pattern matching process, and this enhancement helped maintain a satisfying performance in terms of accuracy while further reducing the false alarm rate; Behera and Panigrahi [19,20] demonstrated the hybrid approach for credit card fraud detection by combining Fuzzy Clustering and NN techniques, and achieved over 93% accuracy with the dataset generated in [27]; APATE was proposed in [21] for automated fraud detection within a large credit card issuer in Belgium; both Luhn's and Hunt's algorithms were employed in [22] for proposing a novel system of credit card fraud detection; the authors in [23] illustrated the use of DM techniques on customer data to add a higher level of authentication to banking processes for real time fraud detection; a hybrid approach combining genetic algorithm and NN was proposed in [24] for Greek companies in the banking sector; a framework named FDiBC was developed in [31] for fraud detection within the Saman Bank in Iran; an e-banking security system employing Cryptography and Steganography was introduced in [25] ...…”

End-to-end neural network architecture for fraud scoring in card payments

“…Different techniques have been proposed to deal with the automatic credit card fraud detection [6,7,8,9,10,11]. How- * Jon Ander Gómez ever, several aspects must be considered when building an engine for card fraud detection.…”

E-Payment Systems Risks, Opportunities, and Challenges for Improved Results in E-Business

“…We have tried to provide some of the research concerned with countering fraud, using different methods and techniques in several types of e-payment as shown in Table 1. As per Prajal Save, et al [8]. they have proposed a system which detect fraud in credit card transaction processing using a decision tree with combination of Luhn's algorithm and Hunt's algorithm In another research for credit card fraud detection Snehal Patil, et al [13] they have proposed decision tree approach which reduces the sum of misclassification costs while selecting the splitting attribute at each nonterminal node is advanced and the act of this approach is compared with the well-known traditional classification models on a real world credit card data set.…”