MAS outlines AI implementation in financial services regulation
MAS outlines AI implementation in financial services regulation | Insurance Business Asia
Insurance News
MAS outlines AI implementation in financial services regulation
How has it used the technology, and what is AI’s long-term role?
Insurance News
By
Kenneth Araullo
Singapore’s financial regulator, the Monetary Authority of Singapore (MAS), has responded to parliamentary questions regarding the potential of artificial intelligence (AI), outlining the tech’s strengths in its regulatory capabilities in the financial services sector.
According to MAS chairman Lawrence Wong, the organisation, which centres its supervision on a risk-based approach, prioritises supervisory resources on financial institutions (FIs) perceived as riskier based on data, frameworks, and established processes.
The evolution of data analytics techniques, particularly artificial intelligence and machine learning (AIML) has significantly expanded MAS’s toolkit, Wong said. By harnessing these technologies, MAS can efficiently interpret vast amounts of data and automate tasks that once required manual processing. AI facilitates the identification of outliers and suspicious networks, enabling a more focused and informed approach to scrutiny.
Wong also outlined two significant areas that have witnessed substantial enhancements through AI integration. Firstly, the regulator has deployed machine learning to optimise risk targeting for supervisory and enforcement actions. The organisation has trained machine-learning models using human-identified traits to analyse market trading data. This assists enforcement officers in identifying and prioritising potential market collusion or manipulation for thorough investigation.
Similarly, AI helps in identifying financial advisory representatives with a higher likelihood of engaging in detrimental behaviours such as mis-selling investments or insurance products. FIs with an elevated risk of such behaviours among their representatives are given priority for deeper supervisory engagement.
Secondly, natural language processing (NLP) has been integrated to enhance the efficiency of supervisory processes. Instead of manual review of extensive textual data like reports submitted by FIs, MAS uses NLP to analyse the content, flagging issues that require the attention of supervisors. Furthermore, NLP is utilised to scan social media, industry reports, and media analysis for emerging news and developments that may necessitate supervisory attention.
Apart from AIML, MAS utilises advanced data analytics to identify suspicious activity networks within the financial system, potentially indicative of money laundering, terrorism financing, or other financial crimes. The regulator proactively engages FIs, alerting them to potential threats and assessing the robustness of their controls. These data analysis techniques, coupled with powerful machine learning tools, allow MAS to effectively sift through high-risk networks and transaction patterns.
Wong also underscored MAS’ commitment to exploring the responsible and secure deployment of the latest technologies, including AIML and generative AI solutions, to continually enhance financial supervision and ensure the stability and integrity of the financial sector.
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