KPMG - Emirates NBD Joint Whitepaper
The Anti Money Laundering (“AML”) landscape is witnessing a period of considerable change as financial institutions (“FIs”) face significant disruption to traditional risk management methodologies.
We are living in the era of the digitization—particularly among banks. The swift evolution of technology is leading to an explosion in the number and volume of transactions, growth in electronic payment methods, and cryptocurrency development. The introduction of new technologies implies increased sophistication in criminal methods of laundering money. To tackle this, most financial institutions are aggressively resorting to increased transaction monitoring in real time and enhanced due diligence of more customers.
This whitepaper focuses on how anti-money laundering processes at banks and other financial institutions can be made more efficient by leveraging the power of data analytics.
The ever-evolving regulatory landscape expects banks and FIs to be more vigilant than ever with customers and their funds. Banks spend millions of dollars every year on support functions – compliance, operations, etc. However, there has been little tangible progress in the battle against financial crime.
AML continues to be a key focus area for regulators across the world. An estimated USD 2 trillion is laundered annually through the global banking system, translating to roughly 2-5% of the global GDP.
Globally, regulatory actions have increased nearly fivefold in the last 10 years. As per Finbold’s recently published figures on fines issued in 2020 on account of AML breaches, the figure stands at USD 14.1 billion.
In early 2021, the Central Bank of the UAE (CBUAE) imposed financial sanctions on 11 banks in the UAE for failing to achieve appropriate levels of compliance relating to AML guidelines and framework.2
Managing ML risk is no insignificant task. However, this directly leads to increased manual intervention, resulting in an inefficient risk management landscape.
FIs are increasingly re-evaluating their traditional AML risk assessment methodologies through the deployment of sophisticated technologies, which utilize the power of robust advanced analytics techniques. The key benefits of deploying such methodologies include reducing ‘noise’ to focus on real high-risk customers and transactions, decreased operational costs due to the smaller number of customers for high-risk review, a better capture rate of bad customers, enhanced efficiency of AML processes, and ultimately, improved customer experience.
While advanced data analytics-based customer risk assessment frameworks are sophisticated and competent, it is only a first step towards leveraging advanced analytical models for AML risk management. As models are implemented, detailed processes must be designed to support the risk landscape, as shown by the models. Ongoing efforts must be made to enhance these models further, by integrating advanced techniques such as network analytics and link analysis to augment their predictive power and make processes more robust.
