Elsevier

Information Fusion

Volume 10, Issue 4, October 2009, Pages 354-363
Information Fusion

Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning

https://doi.org/10.1016/j.inffus.2008.04.001Get rights and content

Abstract

We propose a novel approach for credit card fraud detection, which combines evidences from current as well as past behavior. The fraud detection system (FDS) consists of four components, namely, rule-based filter, Dempster–Shafer adder, transaction history database and Bayesian learner. In the rule-based component, we determine the suspicion level of each incoming transaction based on the extent of its deviation from good pattern. Dempster–Shafer’s theory is used to combine multiple such evidences and an initial belief is computed. The transaction is classified as normal, abnormal or suspicious depending on this initial belief. Once a transaction is found to be suspicious, belief is further strengthened or weakened according to its similarity with fraudulent or genuine transaction history using Bayesian learning. Extensive simulation with stochastic models shows that fusion of different evidences has a very high positive impact on the performance of a credit card fraud detection system as compared to other methods.

Introduction

In today’s electronic society, e-commerce has become an essential sales channel for global business. Due to rapid advancement of e-commerce, use of credit cards for purchases has dramatically increased. Unfortunately, fraudulent use of credit cards has also become an attractive source of revenue for criminals. Occurrence of credit card fraud is increasing dramatically due to the exposure of security weaknesses in traditional credit card processing systems resulting in loss of billions of dollars every year. Fraudsters now use sophisticated techniques to perpetrate credit card fraud. The fraudulent activities worldwide present unique challenges to banks and other financial institutions who issue credit cards. In case of bank cards (Visa and MasterCard) a study done by American Bankers Association in 1996 reveals that the estimated gross fraud loss was $790 million in 1995 [1]. The majority of the loss due to credit card fraud is suffered by the USA alone. This is not surprising since 71% of all credit cards are issued in the USA only. In 2005, the total fraud loss in the USA was reported to be $2.7 billion and it has gone up to $3.2 billion in 2007 [2]. Another survey of over 160 companies revealed that online fraud (committed over the Web or phone shopping) is 12 times higher than offline fraud (committed by using a stolen physical card) [3].

To address this problem, financial institutions employ various fraud prevention tools like real-time credit card authorization, address verification systems (AVS), card verification codes, rule-based detection, etc. But fraudsters are adaptive, and given time, they devise several ways to circumvent such protection mechanisms. Despite the best efforts of the financial institutions, law enforcement agencies and the government, credit card fraud continues to rise. In addition to significant financial losses, the main concern of the law enforcement agencies is that this money is also used to support other criminal activities worldwide. Thus, once fraud prevention measures have failed, there is a need for effective technologies to detect fraud in order to maintain the viability of the payment system. Fraudsters constitute a very inventive and fast moving fraternity. As preventive technology changes, so does the technology of criminals and the way they go about with their fraudulent activities.

The possibility of enhancing existing operations by introducing an effective FDS constitutes the objective of our work.

Section snippets

Related work

The approaches used in detecting credit card fraud mainly include neural network, data mining, meta-learning, game theory and support vector machine.

Artificial neural networks (ANN) have been considered for credit card fraud detection by Ghosh and Reilly [4], Aleskerov et al. [5] and Dorronsoro et al. [6]. Ghosh and Reilly [4] carried out a feasibility study for Mellon Bank to determine the effectiveness of neural network for credit card fraud detection. The authors concluded that it was

Proposed fraud detection system

The proposed FDS may be abstractly represented as a 6-tuple 〈System, C, P, ψ, θLT, θUT〉, where:

  • 1.

    System refers to the target system that is being attacked.

  • 2.

    C = {C1, C2,  , Cn} is the set of credit cards on which fraud detection is performed.

  • 3.

    P = {P(C1), P(C2),  , P(Cn)} is the set of profiles, where each P(Ck) corresponds to the profile of the owner of the card Ck. The profile of a cardholder is a set of patterns containing information like card number, transaction amount and time since last purchase.

  • 4.

    ψ(Tj

Simulation and results

We demonstrate the effectiveness and usefulness of our FDS by testing it with large scale data. Due to unavailability of real life credit card data or benchmark data set for testing, we developed a simulator to generate synthetic transactions that represent the behavior of genuine cardholders as well as that of fraudsters.

It may be noted that Aleskerov et al. [5] tested the performance of their CARDWATCH system on sets of synthetic data based on Gaussian distribution only. Chan et al. [11] have

Conclusions

Though most of the fraud detection systems show good results in detecting fraudulent transactions, they also lead to the generation of too many false alarms. This assumes significance especially in the domain of credit card fraud detection where a credit card company needs to minimize its losses but, at the same time, does not wish the cardholder to feel restricted too often. We have proposed a novel credit card fraud detection system based on the integration of three approaches, namely,

Acknowledgments

We are thankful to the anonymous reviewers for their constructive and useful comments. This work is partially supported by a research grant from the Department of Information Technology, Ministry of Communication and Information Technology, Government of India, under Grant No. 12(34)/04-IRSD dated 07/12/2004.

References (34)

  • Y. Li et al.

    Securing credit card transactions with one-time payment scheme

    Journal of Electronic Commerce Research and Applications

    (2005)
  • W. Roberds

    The impact of fraud on new methods of retail payment

    Federal Reserve Bank of Atlanta Economic Review, First Quarter

    (1998)
  • Statistics for General and Online Card Fraud, 20 June, 2007....
  • Online fraud is 12 times higher than offline fraud, 20 June, 2007....
  • S. Ghosh, D.L. Reilly, Credit card fraud detection with a neural-network, in: Proceedings of the Annual International...
  • E. Aleskerov, B. Freisleben, B. Rao, CARDWATCH: a neural network based database mining system for credit card fraud...
  • J.R. Dorronsoro et al.

    Neural fraud detection in credit card operations

    IEEE Transactions on Neural Networks

    (1997)
  • M. Syeda, Y.Q. Zhang, Y. Pan, Parallel granular neural networks for fast credit card fraud detection, in: Proceedings...
  • S. Maes, K. Tuyls, B. Vanschoenwinkel, B. Manderick, Credit card fraud detection using Bayesian and neural networks,...
  • R.C. Chen, M.L. Chiu, Y.L. Huang, L.T. Chen, Detecting credit card fraud by using questionnaire-responded transaction...
  • R.C. Chen, S.T. Luo, X. Liang, V.C.S. Lee, Personalized approach based on SVM and ANN for detecting credit card fraud,...
  • P.K. Chan, W. Fan, A.L. Prodromidis, S.J. Stolfo, Distributed data mining in credit card fraud detection, in:...
  • R. Brause, T. Langsdorf, M. Hepp, Neural data mining for credit card fraud detection, in: Proceedings of the...
  • C. Chiu, C. Tsai, A web services-based collaborative scheme for credit card fraud detection, in: Proceedings of the...
  • S.J. Stolfo, D.W. Fan, W. Lee, A.L. Prodromidis, Credit card fraud detection using meta-learning: issues and initial...
  • P.K. Chan, S.J. Stolfo, Toward parallel and distributed learning by meta-learning, in: Proceedings of the Workshop on...
  • A.L. Prodromidis, S.J. Stolfo, Agent-based Distributed Learning Applied to Fraud Detection, Technical Report...
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