AI and ML are turning AML from a necessary burden into a business benefit

Compliance is not a term that incites joy for many people, and 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.

While automation in AML has improved efficiency, the traditional rules-based approach to transaction monitoring, in particular, is built on outdated technology and does not serve banks or payments businesses well enough.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.

New compliance for the new normal

Digitalisation has accelerated across financial services in the past year, 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 USD 700 million – almost double the 2019 annual total of USD 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 USD 48 million on Know Your Customer (KYC) and Anti Money-laundering (AML) processes, with US banks spending over USD 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 believe 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.

Traversing the hurdles

The financial services industry is undergoing one of the greatest periods of transformation in history. Banking models that prioritised delivery through branch networks are disappearing, replaced by digital solutions. The potential of this new world is well understood – it can bring personalised services for customers, faster response times, instant payments, available in real-time, around the clock. But gaps between digital solutions, banking systems and customers are widening, providing opportunities for criminals to exploit.

Introducing new approaches to AML in the middle of a wholesale revolution in banking isn’t easy. Traditional banks are held back from introducing new, digital-first approaches by a cocktail of legacy technology stacks, dwindling IT budgets and poor data quality. Organisations will only be able to fully leverage operational efficiencies once they look at the big picture and begin to think holistically about the role of AML and compliance within the broader framework of digital transformation across the enterprise.

Collaboration holds the key

In the battle against money laundering, partnerships hold the key. 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.

Six foundations for sound AML

Our research showed the importance of thinking holistically about the role of AML and compliance, and identifies six foundations of how financial services providers can re-think the connection of compliance and AML technologies to the wider business:

ORGANISATION - As well as implementing new technologies, banks should be breaking down walls inside their organisations and hunting down relevant pools of data across different functions.

TECHNOLOGY - Despite budgetary challenges and inconsistent early results, Faster Payments and blockchain-based international currency transfers mean manual monitoring and dispute resolution processes will no longer achieve the desired results. Introducing AI and ML to your AML strategy is non-negotiable.

IMPLEMENTATION - Unless AI solutions and process automation are employed end-to-end, manual-led processes will continue to introduce errors and inconsistencies – as well as reducing transaction times.

COORDINATION - It’s no longer just about banks. Bank clients and partners need to be fully engaged in the battle against money laundering.

INNOVATION - New approaches to regulation are needed – more flexible, more collaborative.

COOPERATION - Cross-industry collaboration must get better. Banks must now consider both national and international collaborations, sharing data and approaches to combat increasingly sophisticated and international criminal organisations.

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About Livia Benisty

Livia Benisty is Global Head of Business AML at Banking Circle. Livia brings more than 15 years experience in financial crime and AML to the Banking Circle team. She is leading a team to develop stringent AML processes identifying how AI can be applied to enhance the effectiveness of AML, reducing the incidence of false positives. Previously she was Head of AML, EMEA, for Citi's Trade and Treasury Solutions She has also worked at BBVA Group and ComplyAdvantage where she gained experience in the role technology can play in managing and mitigating financial crime risk.

About Banking Circle 

Next-generation provider of mission-critical financial infrastructure, Banking Circle is leading the rise of a super-correspondent Banking network. Banking Circle is a fully licensed Bank able to deliver compliant and secure financial infrastructure at low cost. Clients, including Banks and Payments businesses, can now access real-time payments regardless of borders and regardless of size, allowing them to seize market opportunities without having to commit to significant investment in their own internal infrastructure.