Most people today, in the life of this modern age, make online transactions with their benefits including convenience, rapid payment, and access. However, these online transactions have risks such as fraud, phishing, and data breaches. The increased volume of online transactions tends to invite fraudulent and unethical practices, which endanger users’ privacy and security. With such a situation, illegal access to accounts is possible through vulnerabilities. And siphon money illegally. Improving the existing traditional machine learning models is fundamental to defeat such financial risks. The present research work considers feature-engineered machine learning models using Random Forest and Gradient Boosting algorithms to enhance the performance, make a stable model, and improve learning using deep data processing. Moreover, the understanding of financial risks and costs attached to various payments systems is fundamental to defeat fraud effectively as well as economically. This paper proposes three solutions: a fraud-predictive risk assessment model, machine learning-based fraud detection, and an optimization of economic outcomes. This method is supported by real data from the field.