Securing Digital Finance: Applying Machine Learning for Fraud Analysis

The Fintech industry has grown quickly, resulting forward a revolution in financial transactions and improving user convenience and efficiency. But this quick expansion has also brought about a rise in fraudulent activity, which harmed consumer confidence in digital financial services and resulted in significant losses in terms of money. As a result, in the Fintech sector, the capacity to identify fraudulent transactions is crucial. The purpose of this study is to evaluate how well different machine learning algorithms detect fraudulent transactions in the Fintech industry. The effectiveness of various machine learning methods in fraud detection is assessed using a large dataset comprising 284,807 real credit card transactions. Preprocessing and a range of machine learning models, such as Random Forest, Decision Trees, K-nearest neighbors (KNN), Naïve Bayes, Logistic Regression, and Neural Networks are applied to the data. Our results demonstrate that machine learning-based approaches outperform traditional fraud detection methods significantly, especially after addressing data imbalance through the oversampling process.