AI-Powered Banking in Revolutionizing Fraud Detection: Enhancing Machine Learning to Secure Financial Transactions

Abstract

With the constant evolution of financial fraud techniques, the  demand for advanced technological solutions to secure banking transactions and protect sensitive information is at an all-time high. For this reason, Artificial Intelligence (AI) and Machine Learning (ML) have been adopted as game-changing agents in improving fraud detection and  prevention. These technologies allow for real-time anomaly detection, pattern identification, and predictive  analytics, revolutionizing the conventional approach to fraud detection. This article explores  how AI-enabled fraud detection platforms are transforming the banking industry, specifically in the areas of cyber security, real-time transaction review, and risk management in high-frequency trading. We  present a suite of new techniques to enhance the precision and proficiency of fraud detection models. Specifically, it  includes the Splaso Quash Filter, a data preprocessing approach designed to optimize raw data for machine learning models, and Ripe Horn Twin Fish Optimization, a lucid feature extraction technique that improves the ability of the model to detect the critical variables affecting fraud. It uses the Adaptive Neuro Boosted Forest (ANBF) algorithm which is a combination of the neural network's adaptability and the robustness of the use  of error information from the decision forest to improve the decision accuracy more than the algorithm band in the decision. We also delve into the Hash Blue Hellman Algorithm, a form of cryptography that secures the storage of data and offers a  protective approach for sensitive data transactions. We discuss the implications of using AI for fraudulent activity detection, including regulatory  issues, potential negative consequences, and future trends in banking fraud prevention technologies. Experimentation was conducted  on the Bank Fraud Detection Dataset under a Python environment. From the analysis, it was revealed that the suggested methodology offers secure financial transactions and maintains trust in the banking industry.