Money laundering scandals at APAC banks

By CIOReview Team

Money laundering scandals at APAC banks

The structural trends of the financial world had trembled due to the recent Asian banks money laundering scandals, and the surrounding regions are still going through the consequence. According to the survey reports provided by the Silicon Valley analytics firm FICO with regional banks, more than 90 percent fear about money laundering scandals in the coming years. On tracing the reasons for the money laundering scandals, most respondents said lack of resources was the major reason that Asian Pacific banks remained exposed, while few felt it was the lack of expertise and it might also be the political constraints imposed by the government, and inefficient processes. New types of transactions outside the banking are emerging as money laundering risks; Asian Pacific banks surveyed and rated, cryptocurrency with 33 percent, Shadow banking with 22 percent, and property transactions with 20 percent as posing the biggest risks.       

The respondents had difference in their opinions in bringing down the money laundering scandals, which was a major step to be taken to overcome it. One among five banks felt that increase in fines and penalties would be the most feasible way to improve the financial status, others thought it was necessary to better resource the regulators and rest believed that the best way to tackle money laundering is through introducing Anti-Money Laundering (AML) solutions that use Machine Learning. Implementing these steps, to overcome money laundering, it was found across the banks in Asia that the fines up to USD $100 million dollars were imposed on banks for money laundering compliance failures and also the departure of CEOs and many lenders were shuttered. This made the risk level of the banks very high, the lenders realised the need for the improvement in efficacy of compliance operations with the usage of Machine Learning for AML.

For the better achievement of AML detection, financial sector had to help their employees filter through enormous amount of data, report sceptical activity to regulators and keep upgrading the AML tools. Incorporating machine learning and AI-driven analytics is done to prioritize alerts and accelerate decisions which ensure banks can go beyond what compliance technology can do to catch new types of money laundering solution. 

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