Advanced analytics and innovation in Financial Crime Compliance

The financial services sector is now facing greater challenges from sophisticated criminals who have found ways to profit from an increasingly digitalised economy, accelerated partly due to the COVID-19 pandemic. Efforts to enhance detection by augmenting investments made in artificial intelligence (AI) and machine learning (ML), analytics and robotic process automation (RPA) have paid off. However, more work still needs to be done to ensure that the sector is able to adequately respond and curb various risks including financial crime, and maintain the trust it has established with its relevant stakeholders.

The white paper examines the ongoing journey of UOB’s AI anti-money laundering solution, from proof of concept (POC) to production stage, explaining how it gradually calibrated models for integration into current banking operations. It outlines the justification for the Bank’s investment in advanced analytics, AI/ML and robotics – noting how these have been instrumental in mitigating major disruptions.

Sharing UOB’s transformation story – on its use of innovative technologies to combat financial crime provides insight into the implementation process and challenges experienced. It sheds light on the governance of the technology, the engagement required with stakeholders to build trust in the solutions, and how to integrate these into the business as usual operating environment.

Deloitte and UOB previously published two white papers in 2018 and 2019. The first white paper titled, “The case for artificial intelligence in combating money laundering and terrorist financing” explains how financial institutions (FI) can leverage innovation to manage FCC effectively. The second white paper titled, “The future of financial crime compliance”, depicted the future-state of FCC that incorporates AI, ML, RPA and natural language processing (NLP) to manage evolving financial crime risks.