Singapore's financial district & Fullerton Hotel at sunset.

Singapore’s financial district. Source: Shutterstock

How Singapore polices financial crime with RegTech

  • Ensuring financial transaction security in a commercial hub can be very challenging, hence the application of RegTech by the Monetary Authority of Singapore

The use of regulatory technology — or as it has become more commonly known, ‘RegTech’ — has become commonplace. It’s helping financial authorities to monitor and meet compliance requirements more efficiently, especially as those requirements become more complex, and financially-motivated cybercrime more sophisticated.

The economic impact of poor compliance processes cannot be overlooked, especially in such uncertain times. According to the global anti-money laundering (AML) watchdog Financial Action Task Force (FATF), a number of countries including Southeast Asian states Cambodia and Myanmar had strategic deficiencies in their ability to counter money laundering, terrorist financing, and proliferation financing.

“With regulatory requirements continually changing, industry experts agree it’s imperative that financial firms review their business processes to evaluate compliance and ethics risks at least once a year, while always monitoring recent changes to regulations and laws,” Tokenize Xchange Chief Compliance Officer and industry expert on financial compliance, Jessica Chuah told Tech Wire Asia previously.

Financial hubs like Singapore can often be hives of malicious financial activity, placing even more pressure on regulators like the Monetary Authority of Singapore to stay up to date with regtech.

How are organizations using RegTech?

A recent report by the Financial Stability Board (FSB) outlined a mix of regulatory issues, as well as the RegTech and supervisory technology (SupTech) tools to aid in ensuring compliance. Several of the case studies highlight how the Monetary Authority of Singapore (MAS) applies RegTech to flag suspicious activity that could be criminal.

MAS has developed a network analytics tool which it uses to better analyze higher risk areas and financial institutes, including identifying clusters of entities or individuals that exhibit suspicious behavior.

The data inputs for the network analysis in the initial phase comprises mainly of information from the structured data fields. Unstructured textual data within STRs can also be extracted using NLP (natural language processing) models, and the analysis data will be shared with the country’s financial sector to advance knowledge on suspicious transactions.

MAS also utilizes a model to predict the risk of misconduct for financial advisers. This predictive model uses factors like working experience and misconduct history to identify representatives during onsite inspections, and transaction samples for closer inspection.

Another RegTech tool used by the MAS automates the process of reviewing firms’ trade data by leveraging algorithms and statistics to analyze entire datasets. This can be used to spotlight suspicious trades, earmark statistical outliers, and in concert with market-basket analysis and other techniques can pick up patterns, such as which accounts frequently trade together.

As the central bank and financial regulator of Singapore, MAS has been able to witness the deployment of data analytics to regulate measures linked to COVID-19 too. For instance, data on bank branch locations, customer footfall, wait time and peak hours are collected and visualized on a monitoring dashboard. And this data can be used to monitor social distancing and other pandemic measures being undertaken by financial institutes, and be used to inform if inspection and enforcement actions are necessary.

At the same time, the MAS deployed automation tools using NLP to gather international news and stay abreast of Covid-19 related developments. NLP was also tapped to study consumer feedback on Covid-19 issues, and monitor vulnerabilities in the different customer and product segments.

MAS further collected weekly data from regulated institutions to track the take-up of credit relief measures as the Covid-19 pandemic unfolded. Data aggregation and transformation were automated and visualization allowed MAS to identify pain points and issues for policy analysts to examine in detail.