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Empowering companies to leverage AI to step ahead of fraud

Current anti-fraud tools are lethargic and ineffective when it comes to preventing fraud. At present, tools to prevent money laundering are reactive rather than proactive in their fight against financial crime.

They often merely patch gaps which have previously been exploited by fraudsters by which time it is too late. Ideally, AML should be one step ahead of fraudulent activity, for too long has it lagged behind. However with the ever-expanding applications of AI, the tide may just be turning.

You only need to look at the recent legislative activity of the European Parliament to understand the extent of the problem. Over the past five years, it has adopted 3 wide-sweeping directives targeted at anti-money laundering and reducing terrorism financing.

Adhering to such technical legislation is a challenge for banks who face three key risks when it comes to money laundering activities.

Firstly, the supervisory authorities have the power to issue heavy sanctions on firms for incompetent AML procedures. It has been estimated that between 2008-2018, banks have been fined a total of $26 billion for inadequate checks and safeguarding procedures.

More damaging to banks than financial punishment is the reputational damage that comes with being found guilty of being complacent when it comes to financial crime. Banks found guilty of failed AML measures are percieved by the general public as, at best, incompetent entities incapable of being able to secure its architecture and, at worst, seen as active partners, even facilitators of, financial crime.

Finally, along with the development of cybercrime, frauds and scams committed to the detriment of financial institutions and their clients have an increasing share in the perpetrators' revenues.

AI algorithms take the lead

This position and tarnished reputations of banks is not sustainable and can be attributed to the rise of FinTech banks, such as Monzo & Revolut, who have capitalised on banks' failures to improve their reputations after the 2008 Recession.

In order to improve this position, AI should play a leading role.

The major benefit of using AI is its greater accuracy and speed compared to traditional methods. Not only can it analyse vast swathes of data in a fraction of the time it would take a human to, but it is also far more accurate.

This accuracy and speed is what is crucial. AI greatly reduces the number of false positives (cases that are flagged as fraudulent which, after investigation, turn out to be non-fraudulent).

Reductions in false positives means less time is wasted on investigating non-fraudulent activity and more time spent identifying and preventing the real cases.

AI in AML optimises the existing processes by significantly enhancing the effectiveness of inefficient rule-based approaches. These approaches are often unable to consider complex interdependencies between various activities carried out to money launder.

Instead AI uses a pattern based approach which is constantly being improved by the continuous learning feature of AI which more accurately tracks money laundering than a rigid rules-based approach.

Regulators in the US have encouraged financial institutions to leverage innovative approaches to counter money laundering and fighting financial crime. This flexible approach by the US has given firms freedom to pioneer AI technologies without the fear of regulatory backlash.

However, in other environments, notably Europe, there have been many cases where financial institutions have feared the implementation of artificial intelligence due to uncertainty over regulator reaction.

This uncertainty negatively affects the incentive that firms have to innovate their vetting processes. This results in slow adoption rates of new technology and therefore continues the cycle of being one step behind the fraudsters.

Communication is Key

There is no doubt that the situation highlighted above leads nowhere. Without technological innovation in the compliance area, meeting AML obligations will encounter serious challenges where traditional solutions may be utterly defenseless against technologically-powered, sophisticated fraud.

To expedite the AI transformation of compliance, a deep and substantive dialogue between the regulators and the regulated (representatives of financial institutions) is required. This would allow for transparency of the process helping to alter the relationship from one that is antagonistic to a united front against those that wish to do harm.

This message is of heightened importance given the most recent geopolitical developments caused by the Covid-19 pandemic. There is now a greater need to focus on the acceleration of new methods and tools and therefore allow more trust in the adoption of AI technological infrastructure to better reduce costs, increase agility and actively prevent serious fraud and money laundering cases from happening instead of simply mitigating risks after the facts.


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