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AI money laundering and banks
CreatedWednesday, 22 November 2017
Created byNesrin Ercan
Last modifiedWednesday, 22 November 2017
Revised byNesrin Ercan
Favourites1239 AI money laundering and banks /index.php/site_content/item/1239-ai-money-laundering-and-banks
There are further indications that banks and financial institutions are moving towards using artificial intelligence (AI) to tackle financial crime and boost their anti-money laundering operations.
Early this month (November), OCBC Bank announced that it became the first Singapore bank to tap AI and machine learning to combat financial crime.
OCBC Bank’s transaction monitoring team and its Fintech unit, The Open Vault at OCBC, conducted a proof of concept with Fintech company, ThetaRay, which concluded earlier this year.
The bank is now in an extended proof of concept and pre-implementation phase, involving advanced testing with additional test data to allows it to further verify the efficacy, security and robustness of the solution and gain a better understanding of its workings and capabilities.
Once this is done, the bank aimms to fully implement the technology, which will run in parallel with its existing transaction monitoring system, in the second quarter of next year.
OCBC said the use of these technologies will significantly increase the bank’s operational efficiency and accuracy in the detection of suspicious transactions.
In a separate announcement, Intel said that the Bank of New Zealand (BNZ) has joined its Saffron Anti-Money Laundering (AML) Advisor programme.
In mid-October it launched the Intel® Saffron™ Anti-Money Laundering (AML) Advisor, aimed at detecting financial crime through a transparent AI solution using associative memory.
Intel says it is able to tailor the AI solution specifically to the needs of financial services institutions.
The solution’s associative memory AI simulates a human’s natural ability to learn, remember and reason in real time. It mimics the associative memory of the human brain to surface similarities and anomalies hidden data sources, but is able to access a larger data set than its human counterparts.
Intel says its Saffron AML Advisor is designed to enhance decision-making in very complex tasks, and claims early results indicate they can catch money launderers with “unprecedented speed and efficiency.”
Similarly, OCBC’s says its Fintech solution uses an algorithm that is not reliant on an exhaustive set of programmed rules to flag transactions for review. Instead of looking at each transaction as a standalone, the algorithm is able to “intelligently” detect anomalies in transaction behaviour by assessing broad parameters (scanning products, customers and risks, and diverse data sources) before arriving at a final analysis.
“The software is dynamic and able to ‘learn’ from or adjust to changes in transaction patterns over time, allowing it to flag suspicious transactions with better precision, as well as discovering new patterns for smarter future detection. What this means is a reduction in the volume of transactions flagged that are to be reviewed,” OCBC said.
The technology is also able to cluster alerts by risk levels: This increases the accuracy of detecting suspicious transactions as it allows analysts to prioritise the review of higher-risk alerts.
OCBC said, “In the proof of concept stage, the technology was used to analyse one year’s worth of OCBC Bank’s corporate banking transaction data. The findings showed that it was able to reduce the number of alerts that did not require further review, by 35 percent. Through the categorisation of flagged transactions by their risk levels, the accuracy rate of identifying suspicious transactions increased by more than four times.”
Clearly the main plus points of using AI to help detect money laundering are speed, efficiency and the volumes it can cover.
Gayle Sheppard, vice president and general manager of Saffron AI Group at Intel the amount of data banks and insurers collect is growing at massive scale, doubling every two years.
“While the quantity of data is growing, so are the types and sources of data, which means today much of the data isn’t queried for insights because it’s simply not accessible with traditional tools at scale. Investigators and analysts will depend on transparent AI solutions to meet the ever-growing demands of consistency and efficiency from a business, regulatory and compliance perspective,” she said.
Loretta Yuen, OCBC Bank’s Head of Group Legal and Regulatory Compliance, says financial crimes are evolving in complexity and sophistication.
“This is why we strongly believe in embracing technology and tools that will increase our proficiency in transaction monitoring.”
Another finance data science solution provider Qantaverse says that mitigating financial crime risk is challenging, but managing the risk effectively and efficiently becomes easier through new AI applications and machine learning solutions that complement existing transaction monitoring systems (TMS) in use by financial institutions, advocating its use as part of a holistic and innovative financial crime mitigation approach.
An AI and machine learning solution would have quickly identified groups of suspicious transactions flowing from Guernsey to Singapore rather than relying on static rules-based TMS.
Additionally, a perpetual know-your-customer (KYC) solution could have been utilised with predictive analytics on the customers’ past, present, and future transactional activity. The perpetual KYC function would also incorporate instant adverse media/PEP checks rather than waiting for a yearly to five-year KYC update.
“By supplementing the traditional review of transactional and customer data, financial institutions can greatly enhance their reputational, operational, and legal risk exposure by deploying advanced data science and AI techniques that are available today,” Qantaverse says.