Despite AML enforcement and regulatory gaps, financial institutions in the region can now tap a cosmic opportunity to unite against fraudsters

According to the analysts at Forrester Research in a May 2024 report, “new hotspots have emerged for money launderers, while innovative technology has also emerged to fight against them. Notably, generative AI (GenAI) has catapulted itself into the forefront of this fight.”

Some trends identified it the firm’s research had indicated that disparate regulatory frameworks and non-coordinated enforcement across countries in the Asia Pacific region are contributing to the proliferation of shell companies (and recently, “family offices”), making it easier for criminals and the wayward rich to exploit loopholes and conceal illicit funds.

Furthermore, increased investor interest, a burgeoning crypto market, and regulatory gaps, have caused a surge in crypto-based money laundering. Traders in South Korea, China, and other Asian countries now comprise 70% of Bitcoin trading volumes. Strong investor interest, as well as APAC’s varied regulatory frameworks, are enabling criminals to exploit the perceived anonymity and ease of cross-border transactions that cryptocurrencies offer.

Finally, ‘trade-based’ money laundering is becoming prevalent and increasingly becoming a challenge for banks, due to the region’s significant trade volumes and position as a hub for global commerce. The complex network of suppliers, intermediaries, and financial institutions involved in cross-border trade gives criminals ample opportunities to manipulate invoices, overvalue goods, and transfer illicit funds under the guise of legitimate trade.

Applying AI strategies to enforcement

The growing volume/sophistication of money laundering activities poses tremendous challenge for banks. With tighter AML regulations, financial institutions are now required to perform comprehensive investigations, thereby increasing due diligence workloads.

Here is where three technological pathways can help financial institutions to cope:

  • GenAI solutions: While still in early stages, GenAI is being used in some banks to enhance risk management insights and scores. GenAI is also being utilized in core transaction monitoring and risk decision-making processes.
  • Explainable AI (XAI): This paradigm is increasingly important to anti-money-laundering (AML) diligence to provide transparency and enable financial institutions and regulators to understand how AI algorithms arrive at their decisions. This transparency builds trust, ensures compliance with regulations, and facilitates communication between stakeholders such as customers and partners.
  • Behavioral biometrics: Today, most AML teams rely solely on transactional data to identify money-laundering attempts. Some banks already use behavioral biometrics as additional layers to detect mule accounts before an actual money transfer occurs. These technologies use sophisticated facial and voice recognition and even physical gait analysis to identify people involved in suspicious activities more accurately.

According to the firm’s Senior Analyst, Meng Liu: “financial institutions can’t successfully battle increasingly sophisticated money-laundering risks on their own. Forrester sees that there has been an increase in public and private collaboration on data sharing, with one key example being the Monetary Authority of Singapore’s collaboration with six major banks in the country. The collaboration launched a digital platform (COSMIC), to enable secure sharing of information across banks to deter financial crime.”

Learning from COSMIC

APAC countries can take a leaf from the COSMIC platform playbook. The framework has the following key data security/privacy management characteristics:

  • Information sharing will be justified if customer behavior or transactions raise red flags that cross stipulated thresholds. In this case, all financial institutions party to the AML data sharing collaboration can provide information securely, with links to the customer or transaction.
  • Parties to the scheme must also file a “suspicious transaction report” to their AML authorities and alert others in the industry via a platform watchlist.
  • The thresholds details and permutations of the red flags must be kept strictly confidential among only the participants — in order to prevent criminals from circumventing them. These conditions will also ensure that information-sharing is “strictly restricted to cases of high financial crime concerns”, to ensure the interest and privacy of legitimate customers. Otherwise, for most customer activities, no red flag indicators will be triggered, and details of such customers or transactions are not shareable.
  • Under this collaboration platform, financial institutions will receive statutory protection from civil liability, provided that any disclosures are done in accordance with the specified thresholds, and made in good faith with reasonable care.

By leveraging GenAI and XAI, even large countries in the region can enhance their ability to detect and prevent money laundering, bridge regulatory gaps, and improve coordination across borders, ultimately fostering a more robust and transparent financial system that grows with technological advances.