Enhancing ATM Card Fraud Detection in Nigeria: A High-Performance Model with AI-Based Spending Pattern Analysis and Biometric Authentication

Since Nigeria implemented its new cashless policy and introduced new naira notes, there has been a dramatic rise in ATM card fraud. The widespread use of credit and debit cards makes them easy targets for fraudsters, which worries both financial institutions and their customers. This, in turn, slows down economic growth and breeds mistrust among consumers. There have been multiple efforts to study the problems and proposed remedies using various methods, including traditional methodologies, machine learning, enhanced firewall security, probability, etc. However, data collecting and fraud investigation have both shown that it is difficult to detect and prevent ATM card fraud for the following reasons: most conventional investigators lack the expertise to detect it due to its infrequency and parallel nature, and there is a lack of knowledge about the access point where ATM fraud is committed. In the study, to suggested two methods for detecting ATM fraud: one for static contexts and another for streaming scenarios. The primary objective of this study is to create an intelligent agent-based model that can detect ATM card frauds with high performance. Basically, the model involves 3-factor authentication process to determine if a user should be granted access or otherwise, deny access to a bank ATM, these includes; Password module, AIAgent spending patter detection and lastly Biometric modules authentication. Optimized Bi-Directional Long Short-Term Memory model were applied to historical log files of the ATM cards usage patterns to devise an intelligent data mining classification model. The weight of the proposed model is optimally selected by Mapping based Cuttlefish optimization that improves the classification accuracy. This model was evaluated using confusion matrix with the dataset from the bank. This will help immensely in providing a way forward in curbing the high rate of ATM card fraud in banking sector.