Automated feature engineering tool for fraud detection in financial transactions using deep learning

Abstract

Financial fraud has become increasingly prevalent in modern corporate environments. It involves the deliberate use of deceptive tactics to achieve monetary benefits within various corporations and organizations. Conventional approaches such as manual verifications and inspections, although intended to detect fraudulent activities, frequently demonstrate shortcomings in terms of accuracy, cost-effectiveness, and efficiency. Existing automated fraud detection systems also have limitations. These includes inefficiencies, high costs of implementation, data imbalance, concept drift, false positives and negatives, limited generalizability, and difficulties with real-time processing. The quick and timely detection of fraudulent activities allows financial institutions to mitigate fraudulent conduct before it could lead to financial loss. This research developed an automated feature engineering tool for fraudulent detection. The developed solution involved utilizing deep learning (DL) techniques to analyse transactional data, thus revealing hidden trends that could indicate fraudulent activities. The developed MLP Classifier achieved an accuracy of 99.75%, surpassing the Logistic Regression and Decision Tree models. The model achieved perfect classification, with no errors in predicting fraud or non-fraudulent transactions. The significance of implementing effective fraud detection systems cannot be emphasized, as they serve as protectors of the security and integrity of the financial ecosystem. By providing protection to both financial institutions and cardholders against potential financial instability, these systems strengthen the fundamental basis of confidence on which transactions rely.

Key Words: Automated Feature Engineering, Deep Learning, Financial Fraud Detection, Machine Learning.