A REVIEW ON ATM FRAUD DETECTION TECHNIQUE USING IMAGE PROCCESING IN MAT

Authors: Deepika Khatwa, Mr. Rahul Kaul

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Abstract

We present a novel approach for detecting fraudulent behaviors from automated teller machine (ATM) usage data by analyzing geo-behavioral habits of the customers describe the use of a fuzzy rule-based system capable of classifying suspicious and non-suspicious transactions. We first compute the geographic entropies of ATM cardholders to form customer classes based on these entropies. ATM transactions are spatiotemporal by inclusion of location information. The transition data can be generated by using transaction data from the current location to the next one. Once, the transition data are generated, statistical outlier detection techniques can be utilized. On top of classical methods, we can easily use crisp unsupervised methods to detect outliers in the transition data. We analyze ATM usage dataset which contains around two years’ worth of data, provided by a midsize Turkish bank. We have shown that a significant bulk of ATM users does not leave the vicinity of their living places.

Introduction

Financial fraud detection and prevention have been receiving increasing attention in the past few years, due to the dramatic increase of losses because of fraud transactions every year. Ensuring the security of transactions carried out by banks and other financial institutions is one of the major factors affecting the reputation and profitability of such organizations. Fraud detection activities involve monitoring the behavior of transactions. Fraud prevention means a proactive approach that involves the analysis of transactions before they are completed and identifying if they are fraud or not. Automated teller machines (ATMs), which have given the consumers a quality of life by allowing them to access cash and other financial information, occupy an important position in alternative delivery channels of banking. Since the introduction of the first ATM in 1967, perpetrators have been devising various ways to steal the cash inside the ATMs. According to European ATM Security Team's (EAST) report, card skimming, cash trapping and ATM malware incidents are generally increased worldwide. Besides, it is reported by Europol and EAST that, as the European Union (EU) bankingindustry migrates to the Europay, MasterCard and Visa (EMV) environment, losses caused by illegal domestic transactions in the EU have gradually decreased since 2008. However, at the same time, the level of illegal transactions overseas has seen a sharp increase. The United States of America remains the top location for such losses, followed by Indonesia and Thailand. In order to fight with this situation, a short term solution called GeoBlocking, was recommended by Europol and European Central Bank, which limits the possibility to misuse debit cards in regions without Chip and PIN verification. The implementation of GeoBlocking solution depends on static rules that are location based and this type of solution has been extremely positive from a security point of view. Since the main issue with ATM fraud is misusage of card information, the problem of determining the authenticity of card usage becomes the central point.

Conclusion

To serve the research goal of detecting ATM Card, various image processing techniques have been applied Texture Segmentation method. To summarize, the proposed algorithm initiated the procedure by aligning the base image with the target image, segment the ROI, perform morphology closingWe introduced a methodology for using a Segment image to detect fraudulent ATM transactions based on location information and derived transition data (such as speed). We showed that coupling entropy values with movement related data can yield valuable information to prevent frauds