Identifying suspicious transaction from daily transaction report
Identifying suspicious transaction from daily transaction report
Money laundering is a common problem for financial organisations. Along with some other aspects of underground economic activity, rough estimates have been put forward to give a sense of the scale of the problem. Due to the illegal nature of the transactions, precise statistics are not available and it is therefore impossible to produce a definitive estimate of the amount of money that is globally laundered every year. This study analyzes the statistical data gathered for different types of fraudulent cases which relates to the number of suspicious transactions reported annually, the corresponding activities taken, and the results achieved. The main purpose of this project was to explore the effectiveness of the fraud transactions analysis report by STAR rule engine. After analyzing the past historical data the system need to generate an analysis report based on suspicious transactions. This system would also help the issuer with a better understanding of their current behavior.