AI technology has taken the world by storm. While the technology can do wonders to improve compliance processes, myths surrounding AI’s use have gained weight and forced many to reconsider using it.
Resistant AI, which has built a platform to detect advanced fraud and manipulation and drastically reduce false positives, has released a new whitepaper debunking myths around AI in AML.
It covers five core myths. These are the idea that a firm can be too small for the technology, a lack of quality data, regulators do not like AI, high costs and implementation struggles.
The whitepaper focuses on the use of AI within transaction monitoring.
It argues that the technology strengthens anti-financial crime functions by overcoming inefficient processes, identifying more incidents of suspicious behaviour, promoting quicker decision making, adapting in real-time and facilitating a firm’s scalability. These are just some of the benefits the technology brings to transaction monitoring.
Despite the opportunities it unlocks, the mentioned myths are preventing some from embracing the technology. The whitepaper outlines the critical opportunities for FinTechs that leverage AI for their transaction monitoring processes, in a practical, complementary, and unobtrusive way.
To validate its claims, the whitepaper features interviews with compliance experts from the FinTech sector to discuss how AI can be applied to improve the fight against financial crime.
One of the myths debunked in the whitepaper is that a firm can be too small for AI, with the technology exclusively for larger institutions.
It explains that smaller firms might think their operations do not warrant investment into AI. Instead, they will opt for traditional rules-based systems because they don’t require extensive technical or analytical expertise. These systems can also be bought off-the-shelf or easily developed in-house.
Resistant AI explains that relying on rules-based systems that are ‘good enough for now’ carries risks. Due to being unsophisticated, these systems make companies an ideal target for criminals. They can exploit vulnerabilities and loopholes to do significant damage before the security team can respond.
Secondly, these rules-based systems are reactive as they rely on past experiences. AI, on the other hand, can be proactive and identify new fraud and money laundering behaviours as they occur, minimising potential damage.
It said, “Implementing an AI-powered transaction monitoring tool at an early stage of growth holds immense advantages. Setting good foundations from the start is much easier than upgrading later, once it is incontrovertible that an existing system is not working as it should or is no longer fit for purpose. While rules-based systems can be low-tech and easy to understand, they rely on manual input and ongoing management that can quickly become time-consuming.”
Read the full whitepaper here to see the other myths debunked.
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