While AI may be a key enabler for financial inclusion in Asia Pacific and across ASEAN, it has already demonstrated its power for financial crime in the hands of bad actors.
Can AI advancements also help financial institutions, digital banks and digital payments leaders to develop more sophisticated fraud detection and prevention systems?
We find out from Jaze Goh, Head of Compliance & Risk Management, M-DAQ.
AI is accelerating financial inclusion across ASEAN, especially in Indonesia, but could also be responsible for the rise in financial crimes, such as in Singapore where an upward of S$1.1 billion was lost to scams last year. How is AI and the rise of financial inclusion powering the next wave of financial crimes?
JG: A key challenge facing the financial industry is the surge of AI-driven fraud. The ability of criminals to create highly realistic deepfakes, synthetic identities, and falsified documents and information is making fraud detection increasingly difficult for financial institutions.
It has become a battle of wits. While financial inclusion has granted more people access to financial services, it also presents a vulnerability through increased liquidity, which can be exploited for illicit activities such as money laundering. While AI elevates the sophistication of attacks, financial inclusion expands the potential target base, collectively driving the next wave of financial crimes.
However, with the same AI advancements, financial institutions are also developing more sophisticated fraud detection and prevention systems. By leveraging machine learning, biometric authentication, and real-time data analysis, the industry is continuously strengthening its defenses to stay ahead of evolving threats.
How serious is the rise in money laundering in this region? How can financial institutions and crimefighters leverage AI and machine learning to uncover hidden patterns indicative of money laundering, surpassing traditional rule-based systems?
JG: There has been a rise in money laundering in ASEAN, with increasing reports of illegal gambling operations and other illicit activities. Financial institutions face the challenge of detecting money laundering through traditional rule-based systems, which often rely on fixed thresholds for risk assessment.
AI and machine learning offer a significant advancement by analyzing vast transactional data to identify hidden patterns that are not apparent through conventional methods, such as learning from historical data, recognizing subtle anomalies such as transactions unrelated to a customer’s nature of business, transactions without proper documentation, or connections between accounts/related persons used for illicit activities.
M-DAQ’s CheckGPT leverages AI and machine learning to analyze complex, cross-border transactions, uncover hidden patterns, and flag potential money laundering activities in real-time.
To power these capabilities, we built CheckGPT on MongoDB Atlas, a cloud-native document database designed for AI-driven data processing. MongoDB Atlas’ native Vector Search enhances CheckGPT’s ability to detect suspicious activities using AI-powered pattern recognition, performing high-dimensional searches across unstructured data, such as transaction records, customer profiles, and compliance documents.
This allows CheckGPT to better detect anomalies, link entities, and uncover hidden illicit activity, helping financial institutions combat financial crime proactively.
What can governments and financial institutions do to prevent financial systems from being exploited by AI-empowered cybercriminals?
