Uncovering the business benefits of AI and Machine Learning in AML

Livia Benisty, Global Head of Business AML for payments specialist, Banking Circle, discusses how the latest tech is bringing efficiencies, cost savings and better customer relationships – as well as compliance.

Regulatory compliance is often shouldered as a necessary burden, an inconvenience, even a limitation on the product design and sales teams. Of course, financial institutions are subject to significantly more regulation than many other businesses. However, compliant processes can bring business benefits beyond ticking boxes for the regulators and, most importantly, combating serious financial crime.

Developments made in recent years to increase automation in anti-money laundering (AML) processes have vastly improved efficiency. However, the traditional rules-based automated approach to transaction monitoring is built on outdated technology and does not serve banks or payments businesses well enough. Using these static behavioural rules, which capture only one element of the transaction, the industry sees false positive rates of 97-99%.

Utilising artificial intelligence (AI) and machine learning (ML) enhances the precision of the rules, providing instead a series of indicators which point to something being higher risk. This cuts down false positives – reducing operational workload, enhancing efficiency and freeing up resources to focus on other areas such as customer relationships.

A ‘new normal’ needs new compliance

Digitalisation has accelerated across financial services since the beginning of the COVID-19 pandemic, but regulation and banking processes have struggled to keep up. This has contributed to a rapid increase in money laundering. During the first six months of 2020, global money laundering fines reached more than $700 million – almost double the 2019 annual total of $444 million .

Fines may be small, relative to industry revenues, but the damage they cause to customer trust and business disruption are considerable. Each year, to reduce this damage, banks spend an average of US$48 million on Know Your Customer (KYC) and AML processes, with US banks spending over US$25 billion a year on AML compliance . This is a big spend and could go some way towards explaining why financial institutions have consolidated their strategies to reduce overall risk – in turn disenfranchising certain sectors and communities.

Rejecting a market or sector isn’t really the answer and a wide range of financial institutions have begun to introduce AI-based approaches to combat the rise in money laundering. But this isn’t without its challenges. When we spoke to 300 senior decision-makers in European banks, we found a widespread belief that AI implementation to date has been far too inconsistent, potentially compromising their business objectives.

Adding to the challenge of robust implementation, IT budgets are getting tighter across the board. Despite this, our interviewees said that AI and ML are absolutely essential in the battle against money laundering in the digital future. And looking ahead, our respondents envisage a future in which robotic processes automatically apply machine learning techniques to data harvested across the entire transaction chain, rather than just select parts of the process as at present.

The importance of business-wide buy-in

Complementing the research published in the white paper, Better by design? Re-thinking AML for a digital age, Banking Circle recently hosted a webinar in which a panel of AML experts dug deeper into the challenges in the market today. A key topic in the discussion was how to create a strong culture of AML within a business, and the importance of all stakeholders understanding the importance of compliance.

Antonia Michail, Deputy Anti-Money Laundering Compliance Officer at Nuvei, explained that the best way to get everyone on board with AML processes is to help employees across the business to understand the real-life implications of money laundering and the victims of associated organised crime. Humanising the reasons for fighting financial crime helps employees across the business to fully appreciate the impact their actions or inactions can have on others and the wider business.

But I don’t believe this alone will necessarily lead to an effective AML programme. However well understood, the human impact of money laundering is often forgotten, particularly when onboarding a potentially profitable, yet higher risk client.

In the battle against money laundering, partnerships hold the key. Sales and compliance teams must recognise their common goal of onboarding good, profitable, low risk clients. Working together in a deliberate way allows the teams to gain a full and true picture of risk, to reduce friction and improve the customer experience for good customers.

As well as internal partnerships, financial services providers of all types must now consider both national and international collaborations, sharing data and approaches to combat increasingly sophisticated and international criminal organisations.

A partnership between Dutch banks is one example. Five banks, accounting for 90% of payments executed in the Netherlands, came together to develop an AML solution. In partnership with Deloitte, they created a joint venture which will allow them to pool their transaction data and undertake common analysis of that data. Together they are able to identify patterns and exceptions which could indicate either the presence of money laundering or terrorism financing.

This pooling of data is a vital element of successful AI and ML but it does require that the data is clean, well-labelled and from the right sources. That data must then be managed and interpreted in the right way. Almost one in four (24%) of our respondents cited poor-quality data as a key concern for the success of their IT strategy. And they estimated that up to 15% of real-time transactions are being blocked owing to poor data on either recipients or transaction initiators. For AML processes to be efficient and effective, data quality must improve radically. Cross industry collaboration is the best way to bring about that change.

Download the full white paper, Better by design? Re-thinking AML for a digital age, by clicking here.

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