UPI Transaction Fraud Detection Using Machine Learning

Nowadays, people conduct practically all their business online. While there are many benefits to online transactions, like viability, speedier payments, and ease of use, there are drawbacks as well, including fraud, phishing, and data theft. The increasing volume of internet transactions raises the possibility of fraud and dishonest business practices that compromise an individual’s privacy. When a criminal can take control of an account and move money out of a person’s online bank account. To minimize anticipated financial losses, it is also necessary to enhance traditional machine learning techniques. A feature-engineered machine learning-based model that is Random Forest and Gradient Boosting algorithm which can improve its performance, reinforce its stability, and gain experience by processing as much as data it can. Finally, understanding the costs and risks associated with payment methods is essential to fighting fraud in a methodical and cost- effective manner. We define three models to address these issues: a risk model to forecast fraud risk while taking counter measures into account; machine learning-based fraud detection; and economic optimization of machine learning outcomes. Real data is used to test the models.