The Role of Artificial Intelligence in Risk Management and Fraud Detection in Financial Services

Abstract

An effective solution for executives interested in how increased operational risk management and fraud detection =FS improves the resilience of the financial services industry: Amid unprecedented changes in the financial services market, global financial organizations are increasingly focusing risk management and cybersecurity. This paper reviews anomaly detection as being suitable for the identification of outliers and minimization of the risk of financial losses. This method uses autoencoders, isolation forests and statistical methods to identify anomalous patterns in high dimensionality transaction data. Anomaly detection techniques update themselves with new data and protect against newer types of fraud that a rule-based system can not discern. These methods are also integrated with real time processing frameworks such as apachem kafaka, and spark streaming to minimize the false alarms to achieve better percentage of fraud detection. Examples reveal how the proposed strategy stops credit card fraud and money laundering, too. These are important area of concern in the context of this study and comprise deployment challenges like scalability of the Anomaly Detection System, interpretability of the anomaly detection model, and financial regulations associated with the application of the system.