- India’s RBI is using Artificial Intelligence to prevent financial crimes.
- Nasdaq Automated Investigator for AML is considered the first automated solution for investigating anti-money laundering (AML) and can potentially trigger as many as 200,000 to 300,000 alerts a month in extreme cases.
Bengaluru: Artificial intelligence (AI) can play an important role in fraud detection and money laundering and regulators are working on deploying solutions that can minimise the possibilities of financial crime, a senior official of India’s Central Bank said.
Globally, banks spend about $30 billion every year only to fight financial crimes and as per reports, less than one per cent of all suspicious activity detected by the bank monitoring system yields information that can be used by a regulator or law enforcement agency.
The Reserve Bank of India (RBI) has put in place a central information registry and with data collected over time, the algorithms can tackle offences ranging from cheque frauds to money laundering and be able to intelligently predict possibilities of such occurrences and take preventive measures.
Cheque fraud detection system
“A few weeks back, we introduced the cheque-truncated mechanism. In August this year, we rolled out ‘Positive Pay’ in an effort to identify the misuse of cheques. As the system gets more popular and software develops more, will be easily able to develop the cheque fraud system,” T. Rabi Sankar, Executive Director, Reserve Bank of India, says.
The Positive Pay System for Cheque Truncation System (CTS) will be implemented from January 2021 and involves a process of reconfirming key details of large value cheques.
Under this process, the issuer of the cheque submits electronically, through channels like SMS, mobile app, internet banking, ATM, etc., certain minimum details of that cheque (like date, name of the beneficiary/payee, amount, etc.) to the drawee bank, details of which are cross-checked with the presented cheque by CTS.
Any discrepancy is flagged by CTS to the drawee bank and presenting bank, who would take redressal measures.
National Payments Corporation of India (NPCI) will develop the facility of Positive Pay in CTS and make it available to participant banks. Banks, in turn, shall enable it for all account holders issuing cheques for amounts of Rs50,000 and above.
AI helps investigate AML
Sankar, who was speaking during the recent global AI summit RAISE (Responsible AI for social Empowerment) that was held online, said the advancement of AI technology would also help in detecting money laundering.
“Artificial intelligence should help identify trends in the movement of money, which is key to identify money laundering and similar anti-social service.”
Last month, US Stock Exchange Nasdaq announced that it is deploying a tool to identify and investigate anti-money laundering (AML) and other financial crimes for retail, commercial banks and other financial institutions.
The cloud-deployed Nasdaq Automated Investigator for AML has been designed and built in partnership with UK-based Caspian in which Nasdaq Vantures – the investment arm of Nasdaq has a minority stake.
Considered as the first automated solution for investigating AML for retail and commercial banks and other financial institutions, it can potentially trigger as many as 200,000 to 300,000 alerts a month in extreme cases.
According to Nasdaq, the new system ingests alert data from any transaction monitoring system, collates all of the necessary data required for analysis, analyses the data and then replicates complex human decision making to provide a clear, auditable justification for all alerts regardless of volume, and in seconds.
The technology is powered by multiple variants of artificial intelligence including both supervised and unsupervised learning in conjunction with business rules to provide a significant increase in operational efficiency.
Nasdaq also says it sees broad applicability for this new category of solutions and plans to roll out functionality across complementary financial crime segments in the future.