Advancing Financial Fraud Detection: Exploring the Impact and Innovation of Deep Learning Models

Financial fraud has two direct effects on both economic systems and markets. On one hand, financial fraud contributes to malevolent activities and might be part of organized crimes. On the other, such fraud prevents many honest people from investing and thus might suppress those economic activities that usually stimulate development. On the contrary, since detecting fraud in the rule - based system is challenging, it is difficult to pace the constantly changing approaches utilized by fraudsters. Not long ago, experts in machine learning implemented an abnormality - based methodology and made a scam detection efficiency ratio more precise. This paper illustrates the essence of deep learning models, such as autoencoders and recurrent neural networks (RNNs), that could be used to detect illegal activities occurring in transactions. This paper also provides facts on the possible cons and advantages of such tactics. It also produces a basis that is fair for this to be used to detect more fraud in financial transactions.