Business Challenges

Hard to find new ways of money laundering

Suspected case information relies on manual summaries

Case investigation tasks are difficult to allocate reasonably

Solution Overview

Currently, the anti-money laundering monitoring system adopted by banks mainly uses empirical rules as the main way to extract suspicious transactions against money laundering. Sinodata’s artificial smart anti-money laundering solution is based on supervised machine learning technology to risk score suspicious transactions, reduce the false alarm rate of suspicious transactions, and improve the reporting efficiency of suspicious transactions. Identify complex money laundering gangs that are not recognized by rules based on semi-supervised machine learning techniques to improve anti-money laundering technical capabilities.

Customer Services

Solution Advantages

Identify anti-money laundering gangs to expand suspicious account coverage

Optimize the review process

Optimize the existing empirical rule base

Optimize the investigator manpower and improve efficiency

Improving the case identification accuracy