Enhanced Credit Card Fraud Detection: A Novel Approach Integrating Bayesian Optimized Random Forest Classifier with Advanced Feature Analysis and Real-time Data Adaptation

In the financial industry, credit card fraud is a widespread issue that costs both individuals and businesses a lot of money. Using their capacity to spot patterns and abnormalities in huge datasets, machine learning algorithms have demonstrated their effectiveness as tools for fraud detection. This paper suggests a cutting-edge method, specifically an enhanced Bayesian random forest classifier, to improve the detection of credit card fraud. We solve the shortcomings and difficulties of conventional random forest classifiers by applying Bayesian optimization to optimize the model's hyperparameters. Using a publicly available dataset on credit card fraud, we ran experiments to assess the efficacy of the suggested strategy. The effectiveness of our improved Bayesian random forest classifier was evaluated in comparison to cutting-edge methods. The findings demonstrate a fantastic area under the curve (AUC) of 0.99 and a remarkable accuracy of 99.6%, demonstrating the superiority of our proposed classifier above traditional random forest and benchmark techniques. We also investigate the model's interpretability by looking at the importance of characteristics in fraud detection. This research improves the proposed approach's openness and interpretability by offering useful insights into the underlying components that contribute to fraud detection. Our work shows the effectiveness of the optimized Bayesian random forest classifier, but it's crucial to recognize its limitations and the room for improvement in the future. The application of this method to situations other than credit card fraud detection could be explored in more detail. Additionally, it is important to test the proposed classifier's scalability and robustness on bigger and more varied datasets. Ultimately, our study aids in the creation of trustworthy and efficient fraud detection tools for the financial industry. For fraud analysts and investigators, the proposed Bayesian optimized random forest classifier can be used as a decision support tool. Its versatility makes it a plausible answer for a variety of fraud detection problems that go beyond credit card theft.