An intelligent credit card fraudulent activity detection using hybrid deep learning algorithm

Problem Statement: With the rapid growth of internet usage, businesses, including those in the financial sector, are increasingly providing their services online. Consequently, there has been a significant rise in financial fraud worldwide, leading to substantial financial losses. Detecting threats such as unauthorized access and irregular attacks is crucial in combating this problem. To address this issue, machine learning and data mining techniques have been extensively utilized in recent years. Nevertheless, such strategies are significantly limited by a lack of further research on other hybrid algorithms for detecting financial frauds. Methodology: To resolve such pitfalls, the current work proposes FW-GWO (Fire Work-Grey Wolf Optimization) to select relevant features, while, ANFIS (Adaptive Neuro-Fuzzy Inference System) is used for classification. The study focusses on three kinds of fraud datasets namely loan dataset, credit card dataset and insurance fraud dataset. Results: From the outcomes, it is found that, the proposed system shows 99.87% as accuracy, 0.39 as recall, 0.81 as precision and 0.52 as F1-score for fraud loan dataset, while, the proposed work exposes 99.96% as accuracy, 0.68 as precision, 0.70 as F1-score and 0.72 as recall for credit card fraud and 99.85% as accuracy, 0.83 as precision, 0.55 as F1-score and 0.41 as recall for insurance fraud dataset. Conclusion: Overall analytical outcomes revealed the effectual performance of the proposed system in detecting frauds from the three considered datasets. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.