Artificial Intelligence and Machine Learning in Financial Services: Risk Management and Fraud Detection

Abstract

The field of financial risk management is undergoing a significant transformation due to the advancements in artificial intelligence (AI) and the underlying machine learning (ML) techniques that provide the foundation of AI. These developments hold the potential to revolutionize the way the user’s approach and address financial risk. The expansion of AI-driven solutions has opened up various opportunities for comprehending and managing risk. These opportunities encompass a wide range of activities, such as determining appropriate lending amounts for customers in banking, issuing warning signals to financial market traders regarding position risk, identifying instances of customer and insider fraud, enhancing compliance efforts, and mitigating model risk. The prime objective of this study is to investigate the application of AI and ML in the Financial Services industry, with a specific focus on Risk Management and Fraud Detection. This study proposes an intelligent and distributed approach for detecting Internet financial fraud using Big Data. The methodology entails the utilization of the graph embedding algorithm Node2Vec for the purpose of acquiring knowledge and representing the structural characteristics of the financial network graph in the form of compact vectors with reduced dimensions. This facilitates the intelligent and effective categorization and forecasting of data samples from a dataset of significant magnitude through the utilization of a deep neural network. Based on the study's findings, it was observed that the F1-Score test outcomes obtained from the Node2Vec algorithm range from 67.1% to 73.4%. These results surpass the outcomes achieved by the other two algorithms used for comparison. This finding demonstrates that Node2Vec has greater stability in terms of overall performance and yields superior categorization outcomes.