Published by Gbaf News
Posted on May 10, 2017
6 min readLast updated: January 21, 2026
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Banks are squandering £2.7bn a year chasing false leads because outdated Anti-Money Laundering (AML) systems have never been upgraded, research by AML technology experts, Fortytwo Data, reveals.
Spend by banks and firms in other regulated industries on AML compliance is predicted to hit £6.4bn* globally this year and Fortytwo Data has calculated that, on average, 55% of ‘false positives’ and inefficiencies can be eradicated by the most modern systems, accounting for 42% of institutions’ AML costs. That equates to £2.7bn.
Too many financial institutions depend on legacy systems that rely on stale rules and scenarios that each year generate millions of false positives — red flags thrown up by older systems on transactions that turn out to be perfectly innocent. This forces them to retain armies of staff to investigate and process them.
The firm analysed the likely impact machine learning and big data technology will have on the industry using first-hand intelligence on financial services clients and the success rate of its own platform in reducing false positives. It reduced one bank’s false positives by 97.4% and calculates they can be reduced by a minimum of 20%.
The latest systems promise to have a revolutionary impact on the success of AML efforts worldwide and enable the banks, in parallel with the authorities, to transform the amount of criminal proceeds seized each year. The United Nations Office on Drugs and Crime (UNODC) has estimated that less than 1% of illicit financial flows are seized by the authorities.
Fortytwo Data believes AML spending will peak by 2020 as banks start to refresh their systems in the teeth of huge regulatory obligations. It predicts compliance departments will shrink by an average 75% by 2025 at companies that adopt the latest machine learning technology — delivering huge cost savings.
Machine learning platforms are capable of generating their own rules and independently assess money laundering risk, without the need for human programming. They can rapidly differentiate between false positives and high risk transactions, by analysing the correlation between data points and by modelling human behaviours.
Luca Primerano, Head of Strategy at Fortytwo Data, said: “Banks and financial institutions have been bogged down by legacy AML systems for decades and we estimate that just under £3bn a year is being squandered chasing false positives alone. In our experience, new technologies such as machine learning and big data are cutting the number of false positives by huge amounts, in one case exceeding 97%.
“That’s a colossal amount of time and money that is being wasted chasing ghosts.
“These advances in RegTech represent an opportunity for companies to help reduce the ability of criminals to exploit financial networks for money laundering and terrorist financing around the globe. Machine learning has the potential not only to deliver justice but a multi-billion pound refund for firms every year.”
Banks are squandering £2.7bn a year chasing false leads because outdated Anti-Money Laundering (AML) systems have never been upgraded, research by AML technology experts, Fortytwo Data, reveals.
Spend by banks and firms in other regulated industries on AML compliance is predicted to hit £6.4bn* globally this year and Fortytwo Data has calculated that, on average, 55% of ‘false positives’ and inefficiencies can be eradicated by the most modern systems, accounting for 42% of institutions’ AML costs. That equates to £2.7bn.
Too many financial institutions depend on legacy systems that rely on stale rules and scenarios that each year generate millions of false positives — red flags thrown up by older systems on transactions that turn out to be perfectly innocent. This forces them to retain armies of staff to investigate and process them.
The firm analysed the likely impact machine learning and big data technology will have on the industry using first-hand intelligence on financial services clients and the success rate of its own platform in reducing false positives. It reduced one bank’s false positives by 97.4% and calculates they can be reduced by a minimum of 20%.
The latest systems promise to have a revolutionary impact on the success of AML efforts worldwide and enable the banks, in parallel with the authorities, to transform the amount of criminal proceeds seized each year. The United Nations Office on Drugs and Crime (UNODC) has estimated that less than 1% of illicit financial flows are seized by the authorities.
Fortytwo Data believes AML spending will peak by 2020 as banks start to refresh their systems in the teeth of huge regulatory obligations. It predicts compliance departments will shrink by an average 75% by 2025 at companies that adopt the latest machine learning technology — delivering huge cost savings.
Machine learning platforms are capable of generating their own rules and independently assess money laundering risk, without the need for human programming. They can rapidly differentiate between false positives and high risk transactions, by analysing the correlation between data points and by modelling human behaviours.
Luca Primerano, Head of Strategy at Fortytwo Data, said: “Banks and financial institutions have been bogged down by legacy AML systems for decades and we estimate that just under £3bn a year is being squandered chasing false positives alone. In our experience, new technologies such as machine learning and big data are cutting the number of false positives by huge amounts, in one case exceeding 97%.
“That’s a colossal amount of time and money that is being wasted chasing ghosts.
“These advances in RegTech represent an opportunity for companies to help reduce the ability of criminals to exploit financial networks for money laundering and terrorist financing around the globe. Machine learning has the potential not only to deliver justice but a multi-billion pound refund for firms every year.”
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