Machine Learning-Based Artificial Intelligence Systems for Financial Services Fraud Detection

Abstract

The financial services industry is facing a growing threat from fraud due to the increasing number of internet transactions. Traditional methods of detecting fraud sometimes fall behind increasingly sophisticated schemes. Huge financial losses and reputational harm could result from this. Financial institutions can benefit from AI-based solutions that employ machine learning (ML) techniques in their fight against fraud. Machine learning algorithms are able to out-predict rule-based systems by analyzing large amounts of transaction data for patterns, anomalies, and indications of impending fraud. The use of various machine learning models, such as decision trees, random forests, and neural networks, to demonstrate their strengths and weaknesses in detecting fraud. Data quality, privacy, and the necessity of easily understandable AI models are among topics covered in the study. The paper presents case studies and empirical data to demonstrate how AI and ML have the potential to enhance fraud detection accuracy, decrease false positive rates, and streamline the financial sector as a whole. Finally, it provides recommendations for implementing AI-based scam detection systems in a way that complies with all applicable laws and ethical standards