Konstantin Bodragin, Business Analyst and Digital Marketing Officer at Bruc Bond
Over the last couple of decades, AML has taken centre stage in the banking world. Nowadays, AML, shorthand for anti-money laundering, drives strategic planning and organisational structuring. AML concerns keep many a manager up long into the night, as the risks are huge, the penalties for infractions potentially devastating, and the criminals – especially in the era of COVID-19 – ever more enterprising. While the prevention of money laundering is paramount, the weight and risk faced by financial institutions may feel onerous to many. Luckily, the banking landscape is changing rapidly, with automation and AI making the burden significantly lighter to carry.
Banks and financial institutions face a two-pronged problem. On the one hand, the pace of digital payment is growing exponentially. Much of the world’s trade is now conducted through purely digital conduits. But it’s not only the volume of digital payments and users growing, so is the speed of transactions, with instant payment systems being deployed around the world.
The increases in speed and volume are of course good news for the bottom line, but require significant resources to handle effectively. Resources that many in the banking industry are struggling to provide adequately. The industry is shrinking rapidly, with bank closures, mergers & acquisitions, and a massive reduction in the workforce dominating headlines in the last decade. COVID-19 has only accelerated the trend, with bank after bank announcing imminent layoffs and reductions in trading. With the squeeze on resources, many banks would have struggled to keep up with the increased workload regardless of any other constraints, but here they are faced with the second prong: the complexities of AML.
AML regulations have grown thick and convoluted in recent decades, and with penalties as severe as truly massive fines and personal liability for offending compliance officers, it is taken extremely seriously. And for good reason. Fraudulent and criminal activity is costing the global economy many billions each year, with the lighter end of the spectrum meant to merely enrich the perpetrators, while at the other lies terrorist financing and socially damaging criminality. Nevertheless, it is a significant strain on banks’ already constrained resources, directly at odds with the growing pace of global digital trade.
To alleviate these pains, bankers and financiers of all varieties are scrambling to adopt the newest technologies to combat money laundering effectively, efficiently and with minimal costs. For this, AI seems to be the answer, and everybody wants a piece of the action. In 2020, you would struggle to find a fraud prevention company that doesn’t have the words ‘AI’ or ‘machine learning’ somewhere in its description.
Machine learning, one of the tools underpinning the AI fight against fraud, means the use of algorithms and statistical models to allow computers to perform tasks without specific instructions. In the context of payments, this means allowing computers to make decision related to AML compliance with no human intervention. While letting go of control is a scary prospect for many a financier, it may be the only right thing to do for effective AML implementation, both to prevent money-laundering incidents and to reduce the rate of false positives.
Current statistics indicate that for every fraudulent transaction stopped by a bank’s compliance team, some 20 legitimate transactions are prevented from going through by understandably overcautious compliance officers. Not only does this represent a serious hit to the bank’s bottom line, it wastes whatever precious resources are at the team’s disposal.
With current, manual methods, any suspicious transaction needs to be investigated in a process that can take anywhere from an hour to several days or weeks, often requiring the input of numerous team members and stakeholders across several departments. The cumulative resource drain is palpable, and the end result is that transactions are often rejected not due to any illegality, but because it is simpler, quicker and cheaper to do so. It is simply easier to suspect everyone and reject transactions outright. With AI systems, this process can take an entirely different shape.
Machine learning algorithms learn from human behaviour, create and continuously improve user profiles and use this information to validate transactions. Where this technology shines are with onboarding and transaction verification. Or rather, whenever a known user’s identity needs to be verified. A distinct change in a user’s behaviour is serious cause for alarm and indicates potential fraud, with someone pretending to be a user they’re not.
Unfortunately, AI cannot provide everything we want. When it comes to the cross-border and B2B space, AI is more limited in its uses. While businesses demand increasingly faster account opening and onboarding, the entirety of the process can’t be automated. The problem stems from a difficulty in standardising. Variations in geography, type of business, corporate structures, and even the individuals involved mean that a risk profile must be created for each case individually. Even if the processes could be automated to a higher degree, the risk to reward ratio may mean that the investment in AI isn’t sufficiently attractive. Simply put, financial institutions are rightly anxious about an automated system messing up in complex cases that could lead to massive fines or worse.
Moreover, there exists a question of accountability. When a decision is made by AI, how are you then able to find the exact reason behind why a transaction is not stopped when it should have been – other than to blame it on the algorithm? Using AI makes it very difficult to audit payments, as the fuzzy logic of Machine Learning is almost entirely obscure to us humans.
In short, yes, AI and automation are providing a much-needed breathing room for banks, financial institutions and fintechs looking to alleviate some of the AML burden. However, they are no panacea. Real-life, human bankers will stay with us for a while longer. And for those looking for banking with a friendly face, that may not be such a bad thing after all.
