Deloitte releases whitepaper on the application of machine learning to fight financial crime
HANOI, 13 November 2018 — Deloitte, a recognised global leader in financial crime compliance, has released a whitepaper that assesses the application of machine learning in anti-money laundering (AML) efforts within financial institutions today.
The whitepaper entitled “The case for artificial intelligence in combating money laundering and terrorist financing” highlights key learnings to move machine learning models into production by first developing well-defined proofs of concepts and having them business validated for use within compliance frameworks. In addition, there is a need for increased industry collaboration to manage risks better in the face of rapid and fast-shifting criminal typologies.
“The case for artificial intelligence in combating money laundering and terrorist financing” calls to attention the promise of machine learning as financial institutions guard against increasingly complex financial crimes and as they keep pace with regulatory requirements. According to the whitepaper, there is the need for innovation in compliance to reduce false positives generated by existing rules-based AML transaction monitoring and name screening systems to bring about greater effectiveness in the manner in which criminal typologies are monitored and addressed.
Machine learning techniques and models can be used to identify previously undetected transactional patterns and data anomalies. These complement and enhance the current rules-based AML processes to make it harder for money laundering to take place. Machine learning can also be used to improve matching criteria when screening millions of customer transactions that take place every day. The application of machine learning by financial institutions can result in reduced manual processes, as well as streamlined repetitive tasks and alleviated cost. In turn, this enables compliance teams to focus on higher value work such as issues resolution and also to ensure that policies and procedures are continuously reviewed and updated to reflect the typologies detected across financial institutions.
“With the significant opportunity for financial institutions to invest in new technologies such as artificial intelligence, these same advancements in technology are also capable of being exploited by criminals to move illicit funds in real-time making it hard to monitor and to detect when flowing through the formal financial system.” said Radish SINGH, Financial Crime Compliance Leader, Deloitte Southeast Asia.
“Critical to any financial crime compliance framework then is to ensure the continuous improvement and increased effectiveness of an organisation’s systems. Never done in isolation, when financial institutions, regulators and enforcement agencies work together using new technologies and sharing intelligence and information, the entire ecosystem stands to benefit. It is paramount that international cooperation is prioritised to anchor goals toward fighting financial crime and making and impact that matters,” Radish added.
The whitepaper highlights a case study which depicts a leading ASEAN bank, United Overseas Bank (UOB)’s journey to tap machine learning to augment and to enhance its existing systems to spot and to prevent illicit money flows. UOB collaborated with Singapore-based regulatory technology company Tookitaki Holding Pte. Ltd. (Tookitaki) on an holistic machine learning solution which enables UOB to draw out faster and more precise information to prevent and to detect suspicious money laundering activities.
Following a six-month pilot, UOB engaged Deloitte to validate the conceptual soundness of its pilot, to validate that the machine learning model was fit for purpose, and to compare the performance of the model with the existing rule-based monitoring process.
Victor Ngo, Head of Group Compliance, UOB, said, “At UOB, we continually explore how technology can enhance the way we operate. As a risk-focused organisation, we stand firmly against money laundering and constantly ensure we stay on top of preventive, detective and enforcement measures. The use of new technologies such as artificial intelligence, which includes machine learning, enables us to sharpen the accuracy and effectiveness of our AML risk management. Given the successful results of our pilot to enhance our AML processes with artificial intelligence, we are working with Deloitte to develop a framework of best practices in which artificial intelligence can augment the fight against financial crimes in the banking system.”
“Seeing the progress made by UOB in their pilot programme to use machine learning is proof positive that a holistic company strategy which encompasses technology, new approaches and collaboration will accelerate the response to financial crime and its corresponding complications. The ASEAN region with its diverse businesses and trade will benefit greatly from financial technology and the continued adoption of innovative solutions.” said HO Kok Yong, Financial Services Industry Leader, Deloitte Southeast Asia.
“As international markets become increasingly interconnected and complex, and threats to the global financial landscape continue to rise, we are committed to work with our clients and partners in the financial services industry to build an ecosystem that will strengthen the walls of defence against financial crime,” Kok Yong added.
To read the white paper, please click here.
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