Published by Gbaf News
Posted on June 8, 2020
6 min readLast updated: January 21, 2026
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By John Spooner, Head of Artificial Intelligence, EMEA, H2O.ai
The COVID-19 crisis has forced banking and finance sector IT teams to re-evaluate their ‘business models’ and the associated historic business assumptions, be it around customers, partners or employees. Much of the underlying assumptions are now null and void, and the models need re-building, and fast. Whilst the global banking and finance sector is challenged to re-build their models to prevent internal chaos, and support business growth, criminals continue to exploit fears over the current pandemic. By mid-April 2020, over 500 coronavirus-related scams and over 2,000 phishing attempts had been reported to UK investigators, and the study, overseen by City of London Police, outlined that total losses hit £1.6m.
Money Laundering activity is no exception to this growth in criminal activity. The UK Legal Sector Affinity Group (LSAG) recently published an advisory note, highlighting key Anti Money Laundering (AML) risks and challenges for the legal profession associated with the Covid-19 crisis.
The Head of AML at the Law Society of Scotland, said: “This is an extremely difficult time for all of us, bringing unprecedented and untold challenge and damage to our society both at a human and economic level. Unfortunately, some will choose to exploit any weaknesses or vulnerabilities we may have and will see opportunity in other people’s misfortune.”
However, corporate governance remains a priority, and technology and business leaders need to acknowledge that there are better ways to more effectively address this growing Money Laundering challenge during and post COVID.
Growth of Artificial Intelligence Technology
John Spooner
Artificial Intelligence (AI) has evolved significantly from being a mere technology buzzword, to the commercial reality it is today. The technology is already making a positive impact across many industries, including in the banking and finance industry, a sector that has a reputation for innovation, as progressive firms look to evolve their AI transformation projects.
The use of AI in the sector is changing the business landscape. According to a 2019 Bank of England survey of 500 UK financial institutions, two thirds of respondents were reported to be using machine learning in some way. Banking and finance institutions utilise AI for customer service, risk management, fraud detection and anti-money laundering, while adhering to regulatory compliance.
AI technology has proven to be reliable, especially when it comes to detecting money laundering, and is empowering leading financial services, especially at this time, to address the challenge, in an increasingly efficient and effective way, but there is still room for improvement.
Anti-Money Laundering
Money laundering is defined as “the concealment of the origins of illegally obtained money, typically by means of transfers involving foreign banks or legitimate businesses.” A 2017 report by consulting firm, Quinlan and Associates, outlined that the total US and EU fines on banks’ misconduct, including anti-money laundering violations since 2009 amounted to $342 billion.
Fines for banks that fail to prevent money laundering have increased 500 fold in the past decade, and is now worth more than $10 billion per year. As a result, banks have built large teams, and allocated them time-consuming tasks of investigating any suspicious transactions, which often take the form of multiple small transfers within a complex network of players.
Traditional Approaches to Tackle Money Laundering
Typically, investigation teams use rule-based systems like FICO, Fiserv, SAS AML or Actimize to identify suspicious transactions. This rule-based workflow consists of the following three steps: Firstly, an alert is generated by the alerting system; secondly, the investigator reviews it using information from different sources and finally, the alert is approved as True Positive or classified as False Positive. A False Positive can be defined as an error in data reporting, in which a test result improperly indicates the presence of a condition that in reality is not present.
However, the problem with rule-based systems is that they create a large number of false positives, usually in the range of 75-99%. This means that a vast amount of time is wasted to investigate false alerts. The high number occurs because the rules can become outdated quickly, and it takes time for the systems to be re-coded.
How AI Can Address False Positives
Anti-Money Laundering (AML) programmes that are used in capital markets and retail banking often deploy rule-based transaction monitoring systems, spanning areas across monetary thresholds and money laundering patterns. However, bad actors can adapt to these rules over time, and evolve their methods to avoid detection. This is where AI-based behavioural modelling and customer segmentation can be more effective, in discovering transaction behaviours to identify patterns that indicates potential laundering.
AI, especially time series modelling, is particularly effective at examining a series of complex transactions and finding anomalies. Anti-money laundering using machine learning techniques are able to identify suspicious transactions, and also irregular networks of transactions. These transactions are flagged for investigation, and can be scored as high, medium, or low priority, so that the investigator can prioritise their efforts. As bad behaviour is modified, so does the AI that is underpinning the programmes, meaning the number of false positives stays low while maintaining a high number of true positives.
AI is able to learn from the investigators throughout the review, clearing any suspicious transactions and automatically reinforcing the AI model’s understanding and ability to avoid patterns that don’t lead to laundered money.
AI vs Rule-based Systems for Banking & Finance Firms
AI-powered AML systems provide many advantages over an existing rule-based system. This includes being able to dramatically reduce false positives, provide a curated set of alerts to the investigator and the ability to ingest domain specific IP customised for money laundering. The AI technology can be strategically placed between the AML rule-based system and the investigator, which allows companies to gain a rapid return of investment. Overall, the average investigation time is dramatically reduced from between 45 to 90 days, to seconds.
Address Money Laundering During COVID-19 to Drive Productivity
When used effectively, Artificial Intelligence (AI) can be a critical factor to AML success in the banking and finance sector, whilst at the same time ensuring corporate governance. It enables financial services companies to not only efficiently build personalised banking experiences, fraud and money laundering models but will also improve employee and business productivity. As money laundering networks become ever more complex, and criminal behaviour increases, especially during this crisis, the time is now, for progressive financial intuitions to embrace AI to effectively combat money laundering, and to focus even more effectively on driving overall business productivity for the future post COVID-19 world.
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