An Overview of Bankruptcy Detection Methods Using Artificial Intelligence

Detection of fraud is a very important field of study for online banking, as cyber criminals design new complex fraud attacks on a daily basis, so this requires researchers to constantly develop new fraud detection techniques. Bank fraud is one of the most threatening problems that any human society is facing due to its destructive effects. This refers to the intentional use of false information to defraud another person or organization of money or assets. The banking industry has used law-based systems for decades to detect fraud and humanly investigate transactions. Law-based systems include algorithms that perform various types of detection operations that are manually written by fraud experts. These systems require manual adjustment of scenarios, which challenge the implicit recognition of trading correlations that point to fraud.Given the inherent weaknesses of the law-based fraud detection approach in banks and the limited data used on commonly supervised machine learning algorithms, there is an urgent need for new fraud detection techniques or systems that can counteract the rapid rise of fraud and money laundering. This research uses a review approach and with the aim of examining the methods of detecting bank fraud, examines the research literature and describes the relevant results.