FNB announced on Thursday the launch of Manila, an artificial intelligence (AI) solution to monitor certain regulatory and financial risks, including tax evasion, money laundering, fraud, and insider trading.
Traditionally, this is a time-consuming and laborious process that requires human analysts to gather large swathes of information, review them and generate insights from them, write rationales supporting their thinking, and finally, make decisions about potential or perceived risk.
To deal with this challenge, FNB created an AI-enhanced forensic due diligence process solution called “Manila”
Through Manila, FNB said it can meet all of those regulatory requirements, but decisions can be made more efficiently, faster and produce outcomes with enhanced accuracy.
AI is increasingly becoming a routine part of our daily lives with the introduction of digital personal assistants, music and movie recommendation services, and cars that can be seen around corners. It is also a game-changer for risk management in finance as it provides banks and credit unions with tools and AI solutions to identify potential risks and fraud.
“When it comes to banking, AI can serve a number of purposes, but it’s especially useful for risk management because it’s able to consider a huge array of variables simultaneously, identify patterns of behaviour by extrapolating from massive datasets, provide insights and reviews rapidly that can then be assessed by human analysts, and — ultimately — assist with decision-making while increasing the accuracy of those decisions and reducing false positives,” said Dr. Mark Nasila, Chief Analytics Officer, FNB Risk.
How FNB’s Manila works
The AI system, Manila, gathers data from multiple sources, creates a single view of a customer, and provides insights and a rationale to guide the decision-making process. This can include data from up to 50 sources, including their spending activities and other transactional activities within FNB’s products or platforms.
The AI then checks for any red flags within the organisation’s data about a customer. These include, for instance, anything to suggest a mule account, tax evasion, or links to other high-risk customers.
Manila then starts putting together an insight report on the customer, which it delivers in natural language, and this information then guides the human analyst when they’re doing a review. Manila usually takes between 8 and 13 seconds to generate a forensic synopsis ready for a human analyst to review. Sometimes this process can take as long as a few minutes, depending on how big the dataset being reviewed is, but that’s still hours less than it would take an analyst.
Perhaps most impressive, though, is the AI’s ability to produce a written analysis and a rationale outlining behavioural evidence around financial crime. An analyst uses these to help them make the decision whether a certain customer is high-risk. Because the solution is so consistent in its output, there is a 70% reduction on average from the previous time that was required to provide suitably detailed and thorough quality assurance.
The human analyst, meanwhile, provides quality assurance and the final recommendation, but now in record time.
Dr Nasila said Manila would also enable FNB’s analysts to do quality assurance and approvals remotely, more rapidly, all while maintaining the highest standards.
He added that the adoption of Manila doesn’t mean FNB will need fewer analysts, but rather that their focus is now being used to conduct quality assurance of reviews, and to constantly feedback data into the evolution of the AI.
“On average, the use of AI frees up 70% of analysts’ time and generating a forensic synopsis ready for a human analyst to review that previously took hours can now be completed in as little as 8 seconds.”
