Cloud-Based AI solutions for credit card fraud detection with feedforward neural networks in banking sector

Charles Ubagaram; Karthick.M

doi:10.70454/IJMRE.2021.10101

Authors

Charles Ubagaram Tata Consultancy Services, Ohio, USA Author
Karthick.M Nandha College of Technology, Erode Author

DOI:

https://doi.org/10.70454/IJMRE.2021.10101

Keywords:

Feedforward Neural Networks, Banking Sector, Fraud Detection Systems, Transaction Data, Cloud-Based AI

Abstract

The increasing complexity of fraud strategies, combined with the rapid increase in transaction volumes, makes credit card fraud detection an important area of consideration in the banking sector. Due to the complexities of fraudulent behavior in the context of large transaction sizes, traditional techniques for fraud detection based on rules have become incapable of meeting the challenge. Cloud-based AI solutions based on Feedforward Neural Networks (FNNs) provide the best option when looking for an advanced adaptive approach. This solution benefits from cloud storage to provide highly efficient management of large volumes of transactional data with maximum scalability and flexibility. FNNs aid in differentiating and recognizing complex fraud patterns, while cloud technology ensures that such a system is proficient in adjusting to transaction volume fluctuations. The integration of these technologies resulted in the model achieving a 99.89% accuracy, 98.67% precision, and 95.76% recall. The F1-score of 96.78% shows a balanced performance in precision versus recall. This convergence of AI and cloud technologies promotes accurate fraud transaction detection with reduced false-positive responses and the possibility of continuous adaptation to new fraud schemes. All said, Cloud-Based AI with FNNs is a system that is most potent to secure financial transactions and customers' interests in the banking sector.

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