Hybrid ML Framework for Financial Fraud Detection Using Statistical and DL Models
DOI:
https://doi.org/10.29284/ga5de229Keywords:
FFD, ML, DL, Hybrid Models, AI, Credit Card FraudAbstract
Fraud in the digital financial system is now a big challenge because of the rapid growth of online banking, e-commerce and online transactions. Fraud patterns are dynamic and continually evolving; it is very difficult to uncover fraud using traditional rule-based and simple statistical methods. To overcome this hurdle, in this study, an appropriate hybrid model of ML which uses the DL techniques with the statistical learning models are proposed, which would be effective in detecting financial fraud. The proposed method in this work is a fusion of several DL models (FNN, CNN, RNN) and the statistical models (Random Forest, Logistic Regression and Support Vector Machines) with their advantages. It is based on the pre-processing of data, feature extraction, model training, and evaluation of model performance. Experimental results showed that the hybrid model has achieved better detection accuracy than the conventional ML models. The proposed framework has the potential to significantly benefit banks, insurance companies and other financial institutions by reducing losses and enhancing the security of transactions.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
