Detecting financial crimes using pattern recognition techniques: case of mobile money transactions
Eshiwani, Michelle Mercy
MetadataShow full item record
Financial Crimes have evolved and gained complexity in the recent past owing to advanced technological adoption globally. As consumers have accepted new forms of service delivery that offer them convenience, affordability and easy access, criminals have also found new avenues of pushing their illegal funds or financing criminal activities without raising suspicion or being detected. It is therefore widely recognized that the prevalence of economically motivated crime in many societies is a fundamental threat to the development of world economies and their stability. This research aimed to develop a pattern recognition tool to analyze transaction patterns and detect suspicious transactions. This would in turn reduce the impact of financial crimes on mobile money transactions in terms of loss of revenue for both individuals, corporations and countries by safeguarding legitimate transactions while also tying any loose ends that facilitate the transfer of illegally acquired funds over legitimate channels. This research focused on the field of Pattern Recognition in identifying and analyzing fraud in mobile money transactions. The tool applied Statistical Pattern recognition using the K-Nearest Neighbor algorithm to accurately classify transactions as fraudulent or genuine.