AML Requirement Responsible for £4 Billion in Bank Fines Automated in Ground-Breaking ‘first’ by Big Data Specialist

• Regular customer transaction audits – often known as ‘Account Activity Reviews (AARs)’ – are frequently manual, paper-based and chronically inefficient
• Fortytwo Data estimates automated smart analysis of client accounts could have prevented more than £4bn of banking fines since 2000
• To plug this hole, Fortytwo Data has launched what it believes to be the first automated product using big data and machine learning to bring AAR into the 21^st Century

A third of the £12bn of AML fines* issued since the turn of the century were a result of criminal transactions being missed during manual, and often paper-based customer transaction reviews, according to new analysis by anti-money laundering (AML) specialist Fortytwo Data.

These periodic reviews of customer behaviour and transaction activity – often known as Account Activity Reviews (AARs) – are a regulatory requirement.

This essential process is often time consuming, labour-intensive and carried out independently of other Know Your Customer (KYC) requirements.

As a result it is incredibly expensive, and the fines for criminal transactions that are missed because the process is so inefficient can be huge.

Fortytwo Data’s experts, who use big data and machine learning technology to help banks spot financial anomalies throughout their compliance procedures, estimate missed criminal transactions in the AAR process are responsible for £4bn of fines since 2000.

To plug this hole, in what it believes to be an industry first, Fortytwo Data has launched an ‘Automated AAR’ product for the world’s banks, which brings integration, automation and true machine learning to AAR for the first time.

The new product is capable of carrying out this analysis automatically on a rolling basis, meaning hands-on reviews by employees are only necessary once red flags are raised.

It seamlessly links with existing Know Your Customer (KYC) and AML transaction monitoring systems to enable non-technical users to:

• Rapidly highlight discrepancies between current and expected account activity
• Conduct instant deep-dive reviews of suspicious transactions
• Effortlessly visualise anomalies using charts and key risk indicators
• Automatically update the KYC risk review of each client account based on its findings

AARs are periodic client account reviews that banks are required to carry out to re-rate customer’ risk ratings and check for suspicious activity as a last line of defence.

Every client must be reviewed within certain time limits depending on their level of risk. But Fortytwo Data says traditional AARs are often an ‘afterthought’ at banks stretched by huge regulatory burdens.

They are only a small part of banks’ mandated Know Your Customer (KYC) and AML requirements and because of this are often under-resourced both financially and technologically.

Luca Primerano, Head of Strategy, Fortytwo Data, commented:

“In our experience of working with banks, Account Activity Reviews can often be an afterthought, which is a recipe for disaster given that the regulations surrounding them are as onerous as those relating to AML.

“In many cases these reviews are paper-based, heavily siloed and manually implemented, so it’s no surprise that they are often ineffective as a last line of defence. We estimate that

around £4bn of fines could have been avoided by improving AAR through big data and machine learning.

“To counter this, we’ve launched an ‘Automated AAR’ product, which brings the latest big data and machine learning technology from the high profile world of AML to the low-profile, back-room world of periodic reviews.

“This product enables banks to look far deeper, and at speed, into the way an account is being run and to instantly deep dive into any suspicious transaction relating not just to that specific customer but their entire network. Something had to be done because this highly niche area of compliance can be the last line of defence against financial crime.”

• Regular customer transaction audits – often known as ‘Account Activity Reviews (AARs)’ – are frequently manual, paper-based and chronically inefficient
• Fortytwo Data estimates automated smart analysis of client accounts could have prevented more than £4bn of banking fines since 2000
• To plug this hole, Fortytwo Data has launched what it believes to be the first automated product using big data and machine learning to bring AAR into the 21^st Century

A third of the £12bn of AML fines* issued since the turn of the century were a result of criminal transactions being missed during manual, and often paper-based customer transaction reviews, according to new analysis by anti-money laundering (AML) specialist Fortytwo Data.

These periodic reviews of customer behaviour and transaction activity – often known as Account Activity Reviews (AARs) – are a regulatory requirement.

This essential process is often time consuming, labour-intensive and carried out independently of other Know Your Customer (KYC) requirements.

As a result it is incredibly expensive, and the fines for criminal transactions that are missed because the process is so inefficient can be huge.

Fortytwo Data’s experts, who use big data and machine learning technology to help banks spot financial anomalies throughout their compliance procedures, estimate missed criminal transactions in the AAR process are responsible for £4bn of fines since 2000.

To plug this hole, in what it believes to be an industry first, Fortytwo Data has launched an ‘Automated AAR’ product for the world’s banks, which brings integration, automation and true machine learning to AAR for the first time.

The new product is capable of carrying out this analysis automatically on a rolling basis, meaning hands-on reviews by employees are only necessary once red flags are raised.

It seamlessly links with existing Know Your Customer (KYC) and AML transaction monitoring systems to enable non-technical users to:

• Rapidly highlight discrepancies between current and expected account activity
• Conduct instant deep-dive reviews of suspicious transactions
• Effortlessly visualise anomalies using charts and key risk indicators
• Automatically update the KYC risk review of each client account based on its findings

AARs are periodic client account reviews that banks are required to carry out to re-rate customer’ risk ratings and check for suspicious activity as a last line of defence.

Every client must be reviewed within certain time limits depending on their level of risk. But Fortytwo Data says traditional AARs are often an ‘afterthought’ at banks stretched by huge regulatory burdens.

They are only a small part of banks’ mandated Know Your Customer (KYC) and AML requirements and because of this are often under-resourced both financially and technologically.

Luca Primerano, Head of Strategy, Fortytwo Data, commented:

“In our experience of working with banks, Account Activity Reviews can often be an afterthought, which is a recipe for disaster given that the regulations surrounding them are as onerous as those relating to AML.

“In many cases these reviews are paper-based, heavily siloed and manually implemented, so it’s no surprise that they are often ineffective as a last line of defence. We estimate that

around £4bn of fines could have been avoided by improving AAR through big data and machine learning.

“To counter this, we’ve launched an ‘Automated AAR’ product, which brings the latest big data and machine learning technology from the high profile world of AML to the low-profile, back-room world of periodic reviews.

“This product enables banks to look far deeper, and at speed, into the way an account is being run and to instantly deep dive into any suspicious transaction relating not just to that specific customer but their entire network. Something had to be done because this highly niche area of compliance can be the last line of defence against financial crime.”