Behavioral Analysis of Customer Transaction Patterns in Financial Fraud Detection: An Integrated Machine Learning Approach
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https://doi.org/10.14419/s0r63575
Received date: July 13, 2025
Accepted date: August 14, 2025
Published date: September 1, 2025
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Financial Fraud Detection; Behavioral Analysis; Machine Learning; Anomaly Detection; Customer Transaction Patterns; Risk Management -
Abstract
Financial fraud detection has emerged as a critical challenge in the contemporary digital economy, with sophisticated fraudulent schemes continuously evolving to exploit vulnerabilities in financial systems. This study presents a comprehensive behavioral analysis of customer transaction patterns to enhance fraud detection capabilities through an integrated machine learning approach. Utilizing a dataset of 100 financial transactions encompassing diverse transaction types (purchases, transfers, withdrawals), customer profiles, and monetary values, we develop a multi-dimensional framework for identifying fraudulent activities. Our analysis reveals significant variations in fraud likelihood across transaction types, with transfer operations exhibiting the highest risk profile at 55%, while withdrawal transactions demonstrated no fraudulent activity. Furthermore, fraudulent transactions showed monetary values 28% higher than legitimate transactions, indicating distinct behavioral patterns. The study contributes to the literature by integrating behavioral finance theory with anomaly detection techniques, providing both theoretical insights and practical applications for financial institutions. Our findings demonstrate that customer behavioral patterns, transaction types, and monetary thresholds serve as robust predictors of fraudulent activity, with machine learning models achieving accuracy rates exceeding 94% while maintaining low false positive rates. The results have important implications for real-time fraud detection systems and risk management strategies in financial institutions.
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References
- A. Abdallah, M.A. Maarof, A. Zainal, Fraud detection system: A survey, Journal of Network and Computer Applications 68 (2016) 90-113, https://doi.org/10.1016/j.jnca.2016.04.007.
- A. Acar, H. Aksu, A.S. Uluagac, M. Conti, A survey on homomorphic encryption schemes: Theory and implementation, ACM Computing Surveys 51(4) (2018) 1-35, https://doi.org/10.1145/3214303.
- ACFE, Report to the Nations: 2024 Global Study on Occupational Fraud and Abuse, Association of Certified Fraud Examiners, Austin, TX, USA, 2024.
- A. Adadi, M. Berrada, Peeking inside the black-box: a survey on explainable artificial intelligence (XAI), IEEE Access 6 (2018) 52138-52160, https://doi.org/10.1109/ACCESS.2018.2870052.
- R. Akers, Social Learning and Social Structure: A General Theory of Crime and Deviance, Routledge, New York, USA, 2017. https://doi.org/10.4324/9781315129587.
- L. Akoglu, H. Tong, D. Koutra, Graph based anomaly detection and description: a survey, Data Mining and Knowledge Discovery 29 (2015) 626-688, https://doi.org/10.1007/s10618-014-0365-y.
- K.G. Al-Hashedi, P. Magalingam, Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019, Computer Science Review 40 (2021) 100402, https://doi.org/10.1016/j.cosrev.2021.100402.
- A. Ali, S. Abd Razak, S.H. Othman, et al., Financial fraud detection based on machine learning: a systematic literature review, Applied Sciences 12(19) (2022) 9637, https://doi.org/10.3390/app12199637.
- A.C. Bahnsen, D. Aouada, B. Ottersten, Example-dependent cost-sensitive decision trees, Expert Systems with Applications 42(19) (2015) 6609-6619, https://doi.org/10.1016/j.eswa.2015.04.042.
- A.C. Bahnsen, D. Aouada, A. Stojanovic, B. Ottersten, Feature engineering strategies for credit card fraud detection, Expert Systems with Applications 51 (2016) 134-142, https://doi.org/10.1016/j.eswa.2015.12.030.
- B.M. Barber, T. Odean, Boys will be boys: Gender, overconfidence, and common stock investment, The Quarterly Journal of Economics 116(1) (2001) 261-292, https://doi.org/10.1162/003355301556400.
- N. Barberis, Psychology-based models of asset prices and trading volume, Handbook of Behavioral Economics: Applications and Foundations 1, Vol. 1, North-Holland, Amsterdam, Netherlands, 2018, pp. 79-175. https://doi.org/10.1016/bs.hesbe.2018.07.001.
- V. Barnett, T. Lewis, Outliers in Statistical Data, 3rd ed., John Wiley & Sons, Chichester, UK, 1994.
- Basel Committee on Banking Supervision, Basel III: A Global Regulatory Framework for More Resilient Banks and Banking Systems, Bank for International Settlements, Basel, Switzerland, 2011.
- S.W. Bauguess, M.B. Slovin, M.E. Sushka, Large shareholder diversification, corporate risk taking, and the benefits of changing to differential voting rights, Journal of Banking & Finance 36(4) (2012) 1244-1253, https://doi.org/10.1016/j.jbankfin.2011.11.009.
- M.L. Benson, S.S. Simpson, M. Rorie, J.P. Kennedy, White-Collar Crime: An Opportunity Perspective, Routledge, New York, USA, 2024. https://doi.org/10.4324/9781003175322.
- S. Bhattacharyya, S. Jha, K. Tharakunnel, J.C. Westland, Data mining for credit card fraud: A comparative study, Decision Support Systems 50(3) (2011) 602-613, https://doi.org/10.1016/j.dss.2010.08.008.
- S. Bikhchandani, S. Sharma, Herd behavior in financial markets, IMF Staff Papers 47(3) (2000) 279-310. https://doi.org/10.2307/3867650.
- R.J. Bolton, D.J. Hand, Statistical fraud detection: A review, Statistical Science 17(3) (2002) 235-255, https://doi.org/10.1214/ss/1042727940.
- F. Carcillo, Y.A. Le Borgne, O. Caelen, G. Bontempi, Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics 5 (2018) 285-300, https://doi.org/10.1007/s41060-018-0116-z.
- M. Carminati, R. Caron, F. Maggi, I. Epifani, S. Zanero, BankSealer: A decision support system for online banking fraud analysis and investigation, Computers & Security 53 (2015) 175-186, https://doi.org/10.1016/j.cose.2015.04.002.
- N. Carneiro, G. Figueira, M. Costa, A data mining based system for credit-card fraud detection in e-tail, Decision Support Systems 95 (2017) 91-101, https://doi.org/10.1016/j.dss.2017.01.002.
- R. Chalapathy, S. Chawla, Deep learning for anomaly detection: A survey, arXiv preprint arXiv:1901.03407 (2019), available online.
- V. Chandola, A. Banerjee, V. Kumar, Anomaly detection: A survey, ACM Computing Surveys 41(3) (2009) 1-58, https://doi.org/10.1145/1541880.1541882.
- C. Chimonaki, K. Vergos, J. Soldatos, Perspectives in fraud theories -- A systematic review and comprehensive classification framework, F1000Research 12 (2023) 933, https://doi.org/10.12688/f1000research.131896.1.
- Committee of Sponsoring Organizations of the Treadway Commission, Enterprise Risk Management Integrating with Strategy and Performance, COSO, New York, USA, 2017.
- D.R. Cressey, Other People's Money: A Study in the Social Psychology of Embezzlement, Free Press, Glencoe, IL, USA, 1953.
- R.M. Crowe, The Mind Behind the Fraudsters Crime: Key Behavioral and Environmental Elements, Crowe Horwath LLP, Chicago, IL, USA, 2011.
- A. Dal Pozzolo, O. Caelen, Y.A. Le Borgne, S. Waterschoot, G. Bontempi, Learned lessons in credit card fraud detection from a practitioner perspective, Expert Systems with Applications 41(10) (2014) 4915-4928, https://doi.org/10.1016/j.eswa.2014.02.026.
- C. Dwork, A. Roth, et al., The algorithmic foundations of differential privacy, Foundations and Trends in Theoretical Computer Science 9(3-4) (2014) 211-407, https://doi.org/10.1561/0400000042.
- A. Dyck, A. Morse, L. Zingales, How pervasive is corporate fraud?, Review of Accounting Studies 29(1) (2024) 736-769, https://doi.org/10.1007/s11142-022-09738-5.
- J. Forough, S. Momtazi, Ensemble of deep sequential models for credit card fraud detection, Applied Soft Computing 99 (2021) 106883, https://doi.org/10.1016/j.asoc.2020.106883.
- C. Frydman, C.F. Camerer, The psychology and neuroscience of financial decision making, Trends in Cognitive Sciences 20(9) (2016) 661-675, https://doi.org/10.1016/j.tics.2016.07.003.
- G. Gabrielli, C. Magri, A. Medioli, P.L. Marchini, The power of big data affordances to reshape anti-fraud strategies, Technological Forecasting and Social Change 205 (2024) 123507, https://doi.org/10.1016/j.techfore.2024.123507.
- J. Gee, M. Button, The Financial Cost of Fraud 2019: The Latest Data from Around the World, Crowe UK, London, UK, 2019.
- J.M. Griffin, S. Kruger, What is forensic finance?, Foundations and Trends® in Finance 14(3) (2024) 137-243, https://doi.org/10.1561/0500000073.
- M. Gupta, J. Gao, C.C. Aggarwal, J. Han, Outlier detection for temporal data: A survey, IEEE Transactions on Knowledge and Data Engineering 26(9) (2013) 2250-2267, https://doi.org/10.1109/TKDE.2013.184.
- J. Heese, G. Pérez-Cavazos, C.D. Peter, When the local newspaper leaves town: The effects of local newspaper closures on corporate misconduct, Journal of Financial Economics 145(2) (2022) 445-463, https://doi.org/10.1016/j.jfineco.2021.08.015.
- W. Hilal, S.A. Gadsden, J. Yawney, Financial fraud: a review of anomaly detection techniques and recent advances, Expert Systems with Applications 193 (2022) 116429, https://doi.org/10.1016/j.eswa.2021.116429.
- D. Hirshleifer, Behavioral finance, Annual Review of Financial Economics 7(1) (2015) 133-159, https://doi.org/10.1146/annurev-financial-092214-043752.
- V. Hodge, J. Austin, A survey of outlier detection methodologies, Artificial Intelligence Review 22(2) (2004) 85-126, https://doi.org/10.1023/B:AIRE.0000045502.10941.a9.
- Institute of Internal Auditors, The Three Lines of Defense in Effective Risk Management and Control, IIA Position Paper, Altamonte Springs, FL, USA, 2013.
- S. Jha, M. Guillen, J.C. Westland, Employing transaction aggregation strategy to detect credit card fraud, Expert Systems with Applications 39(16) (2012) 12650-12657, https://doi.org/10.1016/j.eswa.2012.05.018.
- P. Jorion, Value at Risk: The New Benchmark for Managing Financial Risk, 3rd ed., McGraw-Hill, New York, USA, 2007.
- J. Jurgovsky, M. Granitzer, K. Ziegler, et al., Sequence classification for credit-card fraud detection, Expert Systems with Applications 100 (2018) 234-245, https://doi.org/10.1016/j.eswa.2018.01.037.
- D. Kahneman, Thinking, Fast and Slow, Macmillan, New York, USA, 2011.
- D. Kahneman, A. Tversky, Prospect theory: An analysis of decision under risk, Econometrica 47(2) (1979) 263-291, https://doi.org/10.2307/1914185.
- J.M. Karpoff, The future of financial fraud, Journal of Corporate Finance 66 (2021) 101694, https://doi.org/10.1016/j.jcorpfin.2020.101694.
- B. Lebichot, Y.A. Le Borgne, L. He-Guelton, F. Oblé, G. Bontempi, Deep-learning domain adaptation techniques for credit cards fraud detection, Recent Advances in Big Data and Deep Learning: Proceedings of the INNS Big Data and Deep Learning Conference INNSBDDL2019, Sestri Levante, Italy, 2019, pp. 78-88. https://doi.org/10.1007/978-3-030-16841-4_8.
- T. Li, A.K. Sahu, A. Talwalkar, V. Smith, Federated learning: Challenges, methods, and future directions, IEEE Signal Processing Magazine 37(3) (2020) 50-60, https://doi.org/10.1109/MSP.2020.2975749.
- F.T. Liu, K.M. Ting, Z.H. Zhou, Isolation forest, 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy, 2008, pp. 413-422, https://doi.org/10.1109/ICDM.2008.17.
- V. López, A. Fernández, S. García, V. Palade, F. Herrera, An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics, Information Sciences 250 (2013) 113-141, https://doi.org/10.1016/j.ins.2013.07.007.
- S. Makki, Z. Assaghir, Y. Taher, et al., An experimental study with imbalanced classification approaches for credit card fraud detection, IEEE Access 7 (2019) 93010-93022, https://doi.org/10.1109/ACCESS.2019.2927266.
- P. Malhotra, L. Vig, G. Shroff, P. Agarwal, Long short term memory networks for anomaly detection in time series. In ESANN. 89–94.
- B. McMahan, E. Moore, D. Ramage, S. Hampson, B.A. Arcas, Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pages 1273–1282. PMLR, 2017.
- A.J. McNeil, R. Frey, P. Embrechts, Quantitative Risk Management: Concepts, Techniques and Tools, Revised ed., Princeton University Press, Princeton, NJ, USA, 2015.
- S. Nami, M. Shajari, Cost-sensitive payment card fraud detection based on dynamic random forest and k-nearest neighbors, Expert Systems with Applications 110 (2018) 381-392, https://doi.org/10.1016/j.eswa.2018.06.011.
- E.W. Ngai, Y. Hu, Y.H. Wong, Y. Chen, X. Sun, The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature, Decision Support Systems 50(3) (2011) 559-569, https://doi.org/10.1016/j.dss.2010.08.006.
- T. Odean, Are investors reluctant to realize their losses?, The Journal of Finance 53(5) (1998) 1775-1798, https://doi.org/10.1111/0022-1082.00072.
- O.I. Odufisan, O.V. Abhulimen, E.O. Ogunti, Harnessing artificial intelligence and machine learning for fraud detection and prevention in Nigeria, Journal of Economic Criminology (2025) 100127, https://doi.org/10.1016/j.jeconc.2025.100127.
- C. Phua, V. Lee, K. Smith, R. Gayler, A comprehensive survey of data mining-based fraud detection research, arXiv preprint arXiv:1009.6119 (2010), available online: https://arxiv.org/abs/1009.6119.
- T. Pourhabibi, K.L. Ong, B.H. Kam, Y.L. Boo, Fraud detection: A systematic literature review of graph-based anomaly detection approaches, Decision Support Systems 133 (2020) 113303, https://doi.org/10.1016/j.dss.2020.113303.
- PwC, Global Economic Crime and Fraud Survey 2024, PricewaterhouseCoopers, London, UK, 2024.
- N.S. Raghavan, Risk management in banks, Chartered Accountant 51(8) (2003) 841-851.
- K. Randhawa, C.K. Loo, M. Seera, C.P. Lim, A.K. Nandi, Credit card fraud detection using AdaBoost and majority voting, IEEE Access 6 (2018) 14277-14284, https://doi.org/10.1109/ACCESS.2018.2806420.
- A. Roy, J. Sun, R. Mahoney, et al., Deep learning detecting fraud in credit card transactions, 2018 Systems and Information Engineering Design Symposium (SIEDS), Charlottesville, VA, USA, 2018, pp. 129-134, https://doi.org/10.1109/SIEDS.2018.8374722.
- N. Rtayli, N. Enneya, Enhanced credit card fraud detection based on SVM-recursive feature elimination and hyper-parameters optimization, Journal of Information Security and Applications 55 (2020) 102596, https://doi.org/10.1016/j.jisa.2020.102596.
- N.F. Ryman-Tubb, P. Krause, W. Garn, How artificial intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark, Engineering Applications of Artificial Intelligence 76 (2018) 130-157, https://doi.org/10.1016/j.engappai.2018.07.008.
- B. Schölkopf, J.C. Platt, J. Shawe-Taylor, A.J. Smola, R.C. Williamson, Estimating the support of a high-dimensional distribution, Neural Computation 13(7) (2001) 1443-1471, https://doi.org/10.1162/089976601750264965.
- H. Shefrin, M. Statman, The disposition to sell winners too early and ride losers too long: Theory and evidence, The Journal of Finance 40(3) (1985) 777-790, https://doi.org/10.1111/j.1540-6261.1985.tb05002.x.
- H.A. Simon, A behavioral model of rational choice, The Quarterly Journal of Economics 69(1) (1955) 99-118, https://doi.org/10.2307/1884852.
- M. Sokolova, G. Lapalme, A systematic analysis of performance measures for classification tasks, Information Processing & Management 45(4) (2009) 427-437, https://doi.org/10.1016/j.ipm.2009.03.002.
- W.A. Stadler, M.L. Benson, Revisiting the guilty mind: The neutralization of white-collar crime, Criminal Justice Review 37(4) (2012) 494-511, https://doi.org/10.1177/0734016812465618.
- R.H. Thaler, Mental accounting and consumer choice, Marketing Science 4(3) (1985) 199-214, https://doi.org/10.1287/mksc.4.3.199.
- R.H. Thaler, Mental accounting matters, Journal of Behavioral Decision Making 12(3) (1999) 183-206, https://doi.org/10.1002/(SICI)1099-0771(199909)12:3<183::AID-BDM318>3.0.CO;2-F.
- R.H. Thaler, C.R. Sunstein, Nudge: The Final Edition, Penguin, New York, USA, 2021.
- V. Van Vlasselaer, C. Bravo, O. Caelen, et al., APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions, Decision Support Systems 75 (2015) 38-48, https://doi.org/10.1016/j.dss.2015.04.013.
- J. West, M. Bhattacharya, Intelligent financial fraud detection: a comprehensive review, Computers & Security 57 (2016) 47-66, https://doi.org/10.1016/j.cose.2015.09.005.
- C. Whitrow, D.J. Hand, P. Juszczak, D. Weston, N.M. Adams, Transaction aggregation as a strategy for credit card fraud detection, Data Mining and Knowledge Discovery 18 (2009) 30-55, https://doi.org/10.1007/s10618-008-0116-z.
- D.T. Wolfe, D.R. Hermanson, The fraud diamond: Considering the four elements of fraud, The CPA Journal 74(12) (2004) 38-42.
- S. Zeume, Bribes and firm value, The Review of Financial Studies 30(5) (2017) 1457-1489, https://doi.org/10.1093/rfs/hhw108.
- X. Zhang, Y. Han, W. Xu, Q. Wang, HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture, Information Sciences 557 (2021) 302-316, https://doi.org/10.1016/j.ins.2019.05.023.
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How to Cite
Balcıoğlu, Y. S., Merter, A. K., Çelik, B., & Karakaya, T. (2025). Behavioral Analysis of Customer Transaction Patterns in Financial Fraud Detection: An Integrated Machine Learning Approach. International Journal of Basic and Applied Sciences, 14(5), 32-44. https://doi.org/10.14419/s0r63575
