International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 16, No. 3, 8 Jun. 2026
Cover page and Table of Contents: PDF (size: 742KB)
Enhancing ATM Card Fraud Detection in Nigeria: A High-Performance Model with AI-Based Spending Pattern Analysis and Biometric Authentication
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Author(s)
Pradeep B. M.
1,*
Sudeep J
1
Shivashankara S
2
Pavithra D R
3
Ananth G. S.
4
Index Terms
Automated Teller Machine (ATM), Fraud Detection, Bilstm, Cuttlefish Optimization, Biometric Authentication, Artificial Intelligence, AI-Driven Spending Pattern Analysis, Adaptive Authentication
Abstract
One of the effects of the rapid adoption of the cashless policy in Nigeria and the introduction of new naira notes is operational difficulties among financial institutions, which have led to a significant increase in ATM card theft and fraud among clients. Absence of real-time analysis of access points, combined with the intermittent and simultaneous quality of fraudulent dealings, are two major factors that make conventional fraud detection systems fail regularly. Towards reducing ATM fraud, this paper will present a high-performance, intelligent based, AI-based model to integrate three factors of biometric authentication, spending pattern analysis, and password verification into a three-factor model. Results of experiments based on real banking data prove that the proposed solution is superior to traditional models in terms of accuracy, precision, recall, and F1-score. The model uses an optimized Bi -Directional Long Short-Term Memory (BiLSTM) network to analyze historical ATM transaction records and identify behavioral abnormalities that could point to fraud. A Cuttlefish Optimization (MCFA) algorithm that is based on mapping is used to fine-tune the parameters, thus improving the reliability and accuracy of the classification. Biometric verification combined with behavioral modeling using AI stands out as a scalable and dependable framework of minimizing ATM card fraud and instilling confidence within the banking industry.
Cite This Paper
Pradeep B. M., Sudeep J., Shivashankara S., Pavithra D. R., Ananth G. S., "Enhancing ATM Card Fraud Detection in Nigeria: A High-Performance Model with AI-Based Spending Pattern Analysis and Biometric Authentication", International Journal of Education and Management Engineering (IJEME), Vol.16, No.3, pp. 15-28, 2026. DOI:10.5815/ijeme.2026.03.02
Reference
[1]S. Degadwala and P. T. Bharatbhai, “Advancements in ATM Security for Movement and Tampering Detection: A Review,” 2024. DOI: 10.32628/CSEIT2410587
[2]S. Gnanavel, N. Duraimurugan, and M. Jaeyalakshmi, “Smart Surveillance System and Prediction of Abnormal Activity in ATM Using Deep Learning,” in Proc. Int. Conf. on Data Science and Network Engineering, Singapore: Springer Nature, pp. 143–154, 2023. DOI: 10.1007/978-981-99-7605-8 11
[3]N. Abirami, G. Radhika, and N. Radhika, “Automated Teller Machine Security and Robbery Prevention Based on Human Behaviour Analysis,” in 2023 Innovations in Power and Advanced Computing Technologies (i-PACT), IEEE, pp. 1–6, 2023. DOI: 10.1109/i-PACT58649.2023.10434319
[4]C. Chethana and P. K. Pareek, “Analysis of Credit Card Fraud Data Using Various Machine Learning Methods,” in Big Data, Cloud Computing and IoT, Chapman and Hall/CRC, pp. 103–116, 2023. DOI: 10.1201/9781003333781-8
[5]M. M. R. Kumar, M. Tejaswi, and K. Haritha, “Deep Learning and Image Processing Based ATM Security and Identifying the Face,” Journal of Science & Technology (JST), vol. 8, no. 4, pp. 46–52, 2023. DOI: Not available
[6]A. S. Katragada, K. Vuribindi, and V. L. Erickson, “Real-Time Suspicious Activity Detection on ATMs Using Multi- model YOLO Object Detection,” in Proc. 2023 Int. Conf. on Computational Science and Computational Intelligence (CSCI), IEEE, pp. 1254–1258, 2023. DOI: 10.1109/CSCI62032.2023.00231
[7]M. S. Swetha, M. S. Muneshwara, A. Raj, A. Tomar, A. Prakash, and C. Singh, “Enhancing ATM Security in the Forensic Domain Using Artificial Intelligence,” in Artificial Intelligence and Blockchain in Digital Forensics, River Publishers, pp. 81–97, 2023. DOI: 10.1201/9781003369186-5
[8]A. Lubna, A. S. HV, and K. N. Manjunath, “ATM Weapon, Fraud Detection and Cybercrime Prediction,” in Proc. 2023 Int. Conf. on Advances in Electronics, Communication, Computing and Intelligent Information Systems (ICAE-CIS), IEEE, pp. 528–533, 2023. DOI: 10.1109/ICAECIS59453.2023.10471407
[9]S. Stephe, V. Revathi, B. Gunapriya, and A. Thirumalraj, “Blockchain-Based Private AI Model with RPOA-Based Sampling Method for Credit Card Fraud Detection,” in Sustainable Development Using Private AI, CRC Press, pp. 261–277, 2025. DOI: 10.1201/9781003515002-15
[10]J. B. Idoko, M. Mohammed, and A. U. Mohammed, “Machine Learning Based Cardless ATM Using Voice Recog- nition Techniques,” in Machine Learning and the Internet of Things in Education: Models and Applications, Cham: Springer Nature Switzerland, pp. 75–84, 2023. DOI: 10.1007/978-3-031-32653-0 6
[11]R. Raman, S. Prabhakar, and T. Bernatin, “IoT-Based Anti-Theft Controlling and Security System for ATM Machine,” in Proc. 2023 5th Int. Conf. on Inventive Research in Computing Applications (ICIRCA), IEEE, pp. 1272–1277, 2023. DOI: 10.1109/ICIRCA57980.2023.10220954
[12]K. K. Almuzaini, S. Joshi, S. Ojo, M. Agrawal, P. Suman, P. K. Pareek, and P. K. Shukla, “Surveillance Monitoring Based Routing Optimization for Wireless Sensor Networks,” Wireless Networks, vol. 30, no. 6, pp. 6069–6087, 2024. DOI: 10.1007/s11276-024-03664-0
[13]S. Mandavilli, A. Pallayil, S. Evre, A. Mane, and M. Parmar, “ATM Surveillance System Using Machine Learning,” Grenze Int. J. of Engineering & Technology (GIJET), vol. 9, no. 2, 2023. DOI: Not available
[14]N. Joshi, S. Martha, S. Chaudhary, P. Consul, and S. K. Singh, “Deep Neural Networks Based Security Solution for ATM Transactions,” in Int. Conf. on Recent Trends in Image Processing and Pattern Recognition, Cham: Springer Nature Switzerland, pp. 265–275, 2023. DOI: 10.1007/978-3-031-50997-1 22
[15]K. Rathor, S. Vidya, M. Jeeva, M. Karthivel, S. N. Ghate, and V. Malathy, “Intelligent System for ATM Fraud Detection Using C-LSTM Approach,” in Proc. 2023 4th Int. Conf. on Electronics and Sustainable Communication Systems (ICESC), IEEE, pp. 1439–1444, 2023. DOI: 10.1109/ICESC57686.2023.10193492
[16]S. P. Panimalar, M. A. Kumar, and N. Rohit, “ATM Theft Detection Using Artificial Intelligence,” in Proc. Int. Conf. on Emerging Trends in Expert Applications & Security, Singapore: Springer Nature, pp. 517–526, 2023. DOI: 10.1007/978-981-99-1909-3 45
[17]M. N. Alatawi, “Detection of Fraud in IoT-Based Credit Card Dataset Using Machine Learning,” Machine Learning with Applications, vol. 19, p. 100603, 2025. DOI: 10.1016/j.mlwa.2024.100603
[18]K. Sathupadi, S. Achar, S. V. Bhaskaran, N. Faruqui, and J. Uddin, “BankNet: Real-Time Big Data Analytics for Secure Internet Banking,” Big Data and Cognitive Computing, vol. 9, no. 2, p. 24, 2025. DOI: 10.3390/bdcc9020024
[19]M. Doe, E. G. Domanaanmwi, Y. Afriyie, A. A. Bayor, C. B. Baatuomu, and J. J. Moyom, “Enhanced Fingerprint- Based ATM Security Using Machine Learning Algorithms,” in 2024 IEEE SmartBlock4Africa, pp. 1–6, IEEE, 2024. DOI: 10.1109/SmartBlock4Africa63388.2024.10798763
[20]A. I. B. Ramli, “Big Data and Artificial Intelligence to Develop Advanced Fraud Detection Systems for the Financial Sector,” Int. J. of Advanced Cybersecurity Systems, Technologies, and Applications, vol. 8, no. 12, pp. 31–44, 2024. DOI: Not available
[21]S. S. Rafi, M. S. Arafat, R. Islam, M. S. Jalil, M. A. M. Jony, and F. Hossen, “Machine Learning in Financial Fraud Detection: New Models for Predictive Analysis and Mitigating Business Risks,” 2024. DOI: Not available
[22]Y. Vivek, V. Ravi, A. A. Mane, and L. R. Naidu, “ATM Fraud Detection Using Streaming Data Analytics,” arXiv preprint, arXiv:2303.04946, 2023. DOI: Not applicable (arXiv preprint)
[23]A. Thirumalraj, K. Aravinda, E. S. M. El-Kenawy, and N. Khodadadi, “ScatterNet-Based IPOA for Predicting Violent Individuals Using Real-Time Drone Surveillance System,” in Industry 6.0, CRC Press, pp. 182–204, 2023. DOI: 10.1201/9781003346958-10
[24]S. Kiruthiga, R. P. Narmadha, M. A. A. Walid, S. N. Bhat, G. S. Gopika, and P. V. Upadhye, “An Efficient IoMT-Based Heart Disease Prediction System Using Cuttlefish Algorithm with Cascaded LSTM,” in Proc. 2023 5th Int. Conf. on ICIRCA, IEEE, pp. 1441–1447, 2023. DOI: 10.1109/ICIRCA57980.2023.10221018
[25]J. P. Appadurai, T. Rajesh, R. Yugha, R. Sarkar, A. Thirumalraj, B. P. Kavin, and G. H. Seng, “Prediction of EV Charging Behavior Using BOA-Based Deep Residual Attention Network,” Revista Internacional de Me´todos Nume´ricos para Ca´lculo y Disen˜o en Ingenier´ıa, vol. 40, no. 2, p. 16, 2024. DOI: 10.23967/j.rimni.2023.11.002
[26]A. Fiore, F. De Santis, A. Perla, P. Zanetti, and F. Palmieri, “Using Generative Adversarial Networks for Improving Classification Effectiveness in Credit Card Fraud Detection,” Information Sciences, vol. 479, pp. 448–455, 2019. DOI: 10.1016/j.ins.2018.11.008
[27]S. Roy, A. Sun, J. Mahoney, L. Alonzi, and P. Adams, “Deep Learning Detects Fraudulent Transactions in Real Time,” Expert Systems with Applications, vol. 133, pp. 53–66, 2019. DOI: 10.1016/j.eswa.2019.05.027
[28]E. Jurgovsky, M. Granitzer, S. Ziegler, S. Calabretto, P. Portier, L. He-Guelton, and O. Caelen, “Sequence Clas- sification for Credit-Card Fraud Detection,” Expert Systems with Applications, vol. 100, pp. 234–245, 2018. DOI: 10.1016/j.eswa.2018.01.037
[29]V. Pozzolo, O. Caelen, Y.-A. Le Borgne, S. Waterschoot, and G. Bontempi, “Learned Lessons in Credit Card Fraud Detection from a Practitioner Perspective,” Expert Systems with Applications, vol. 41, no. 10, pp. 4915–4928, 2014. DOI: 10.1016/j.eswa.2014.02.026
[30]D. Varmedja, M. Karanovic, S. Sladojevic, M. Arsenovic, and A. Anderla, “Credit Card Fraud Detection—Machine Learning Methods,” Mathematics, vol. 7, no. 10, p. 955, 2019. DOI: 10.3390/math7100955