The Application of AI And Machine Learning in the Financial Industry and its Effects on Risk Management and Fraud Detection

Abstract

This literature review research paper examines the application of AI and machine learning in the financial industry and its effects on risk management and fraud detection. The study conducts a comprehensive search of academic and industry sources and identifies key findings and trends related to the use of these technologies in the financial industry. The literature review finds that AI-based systems have been shown to improve the efficiency and effectiveness of fraud detection by analysing vast amounts of data in real-time and identifying patterns and anomalies that may indicate potential fraud (KPMG, 2018). Additionally, machine learning algorithms can be trained to adapt and improve over time, making the detection process even more accurate. Furthermore, AI-based systems have also been shown to improve risk management in the financial industry (Deloitte, 2019). However, it is important to note that while AI and machine learning have the potential to significantly improve risk management and fraud detection in the financial industry, they are not without limitations and potential biases (Bolton and Hand, 2002; Bose, 2018). Therefore, financial institutions should carefully consider the implementation and use of these technologies and ensure proper governance and controls are in place. This literature review provides a comprehensive understanding of the current state of AI and machine learning in the financial industry, its benefits and challenges, and future possibilities. Keywords: AI, Machine Learning, Financial Industry, Risk Management, Fraud Detection, Governance, Ethical Implications