Integrating Explainable AI in Financial Fraud Detection Systems for Enhanced Decision Transparency
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V6I4P113Keywords:
Explainable AI, Fraud Detection, Financial Security, Artificial Intelligence, Machine Learning, PCAAbstract
The high rate of development of digital financial services has increased the vulnerability of financial systems to fraud. Despite the effect of traditional machine learning models on fraud detection, it is often not flexible and transparent to the evolving financial landscape and regulatory requirements. This research paper is a description of a new way of detecting financial fraud and is more effective than the previous one. It uses the Kaggle credit card data that comprises over 284,000 transactions yet only 492 frauds. The data set was preprocessed in different ways, like label encoding, Min–Max normalization, PCA-based feature selection, SMOTE balancing, and other methods, and then the models were trained. Two significant models, Decision Tree and Multilayer Perceptron, were developed and compared with the existing models (GBM, ANN, LR, NB) on the measurements of accuracy (ACC), precision (PRE), recall (REC), and F1-score (F1). MLP performed optimally with the highest accuracy of 99.52% and the DT model performed optimally in recall and F1-score since it could effectively detect fraudulent cases. SHAP and LIME were the keys to explaining the determinants of the model processing by the use of explanatory AI technologies. Overall, the results revealed that the accuracy of predictions and reproducibility are the cornerstones of building effective fraud detection systems in the finance sector, where the real-life context is vital
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