AI-enabled anti-financial crime

AI, like all good tools, needs to be kept sharp to be effective

How will you ensure your AI-enabled anti-financial crime tools remain effective?

In recent years, AI-enabled anti-financial crime solutions such as machine learning (ML) have become increasingly prevalent in the fight against financial crime. These technologies are used in many sectors, including financial services (FS). As with all technologies, there are benefits and some drawbacks to consider.

AI and ML, along with advanced hardware, can quickly analyse large data sets to identify suspicious behaviour like fraud. This helps firms minimise financial losses, reputational damage, and regulatory fines. It helps firms understand risk exposure and create effective anti-financial crime strategies, reducing costs through AI-enabled automation of investigations and client due diligence.

AI and ML detection algorithms can be complex, making it challenging to identify potential biases or errors, which may cause false positives or negatives. AI/ML models are heavily reliant upon good quality, accurate and up-to-date data to help ensure they are accurate and reliable. Often, AI/ML models need large amounts of sensitive data, and the algorithms themselves can also be open to cyberattacks and data breaches. You must effectively maintain your AI/ML tools to reduce the risk of financial crime.

As with all tools, your organisation will need to adapt successfully and learn to use AI/ML tools effectively in its fight against financial crime.

How do you keep your AI-enabled anti-financial crime tools sharp?

There are a number of processes that can be put into place to help, including:

• Data quality – ensuring the data made available for both training and analysis of your AI/ML solution is complete, accurate, relevant and timely;
• Regularly monitoring AI/ML models to help ensure they are accurate and effective and undertaking remediation where necessary;
• Model validation and verification throughout its life, especially prior to its implementation, and updates to help ensure it complies with all relevant financial crime regulations and operates as expected;
• Having ‘human eyes’ to provide oversight and ensure the models’ fairness, especially when they are updated; and
• Having robust cybersecurity processes in place to help protect against potential breaches.