FINANCIAL FRAUD DETECTION BASED ON HUMAN BEHAVIOR ANALYSIS

Detection of financial fraud is now a cause of major concern in the financial and banking industry because fraud techniques are becoming highly sophisticated. Classical rule- based systems are generally ineffective in detecting complex patterns of fraud, which call for more complex machine learning and artificial intelligence processes. The following paper discusses the different methodologies in detecting financial fraud, ranging from supervised and unsupervised learning to anomaly detection and deep neural network models. Also, it discusses how big data analytics and real- time monitoring of transactions contribute to making fraud detection more accurate. Key issues like biased datasets, new patterns of fraud, and AI model interpretability are also underscored in the research. Using data-driven method- ologies, financial institutions can enhance rates of fraud detection, minimize false positives, and make financial transactions more secure in general.