Supervised and Unsupervised Learning for Fraud Detection in Banking Transactions

The complexity and volume of financial transactions is increasing, making it increasingly important to identify fraud in banks. This project considers the use of machine learning technology to improve detection of fraudulent activity in financial data. To detect and stop fraudulent activity more effectively, this study uses hidden patterns and anomaly detection using a variety of machine learning algorithms, including unsupervised learning techniques, using unsupervised techniques such as clustering and anomaly detection. Monitored techniques such as transaction behavior anomalies, logistic regression, decision trees, random forests, and gradient boosts are used to construct predictive models from specified transaction data. Today, the amounts are being transferred through increasingly complex remedies, so it is more important to track bank fraud more than ever. This study supports the hypothesis that advanced algorithms in self-learning computer programs recognize suspicious activity in bank details than older techniques. Researchers tested many methods: some were learning-based fraud analogies (decision tree, random forest), while others were non-copying an overview of recognition. With integration, these different methods can help banks minimize false positive results while simultaneously maximizing fraud detection. The researchers conducted experiments on actual bank details and their valuation metrics. The valuation metric was based on the degree of fraud detection associated with innocent transactions detection. The results of this study showed a significant improvement in accuracy and efficiency. This indicates that these methods should be used in banking. The implementation of such fraud management measures serves as a robust response to sophisticated financial crimes, allowing banks to carry out seamless business activities.