A solution architecture for financial institutions to handle illegal activities: a neural networks approach

The banking and financial services industry today relies heavily on the use of networked computerized data systems to manage financial accounts and information on a real-time basis for millions of customers. This underlying technology is a source of a large quantity of information that can be used in the identification and prevention of financial fraud involving the illegal/unauthorized transfer of funds by entities external and internal to the victim financial institution. This paper develops a concept involving the use of neural networks to correlate information from a variety of technological and database sources to identify suspicious account activity.