Realtime Fraud Detection in the Banking Sector Using Data Mining Techniques/Algorithm

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] ...…”

Fraud Detection Using the Fraud Triangle Theory and Data Mining Techniques: A Literature Review

“…The framework allowed fraud identification by using intelligent agents, data fusion techniques, and various data mining techniques. In [67], the authors proposed the detection of bank fraud through data extraction techniques, association, grouping, forecasting, and classification to analyze customer data to identify patterns leading to fraud. To conclude this group of papers, West et al suggested that a higher level of verification/authentication can be added to banking processes by identifying patterns.…”

Detect Frauds in Credit Card using Data Mining Techniques

“…As per the authors association rule mining is that technique which is created by interpreting data for frequent if/then pattern and to spot the foremost relationship the confidence and criteria support is used. Suchita Anand Padvekar, Pragati Madan Kangane, Komal Vikas Jadhav [6] has worked on the hidden markov model that are used throughout the transactions through which high fraud coverage as well as low false alarm rate are controlled. Saravanan Sagadevan, Nurul Hashimah Ahamed Hassain Malim and Ong Shu Yee [7] has mentioned about the supervised based classification using the Tree Augmented Naïve Bayes, Bayesian Network Classifiers namely K2, Logistic and J48 classifiers.…”