How AI is transforming financial crime detection

How AI is transforming financial crime detection

As the financial market hits a very turbulent period, the risk of financial crime will only increase. But how can firms ensure they are prepared?  

Resistant AI and ComplyAdvantage have released a practical guide to anti-money laundering to help firms improve their protections. The report, ‘A Practical Guide to AI for Financial Crime Detection’, can be downloaded here and explores how AI transaction monitoring is helping banks.

The report covers four key areas. The first is the value of AI for fraud and money laundering detection in banking through practical use cases and insights. It also explores why pre-emptive risk management is key, as well as how to connect the abstract potential of AI in financial crime risk management to regulatory expectations. Finally, it covers a practical and actionable roadmap for deploying AI in banking.

The report begins by stating the current world climate has made the risk landscape for financial crime very difficult to predict. This is then coupled with the fact financial institutions are trying to evolve their operations. Whether it is digitising their services, incorporating new payment methods, launching new digital wallet services or getting involved with cryptos.

Yet another layer to this is the increasingly tougher regulatory environment banks need to be compliant with.

The report states, “The teams responsible for detecting financial crime are burning out. Up against a ruthlessly dynamic risk landscape with rulesets proliferating beyond control, they’re forced to monitor alert logs that generate so much noise they make actual risks harder to detect. Banks need a new approach to detecting financial crime.”

The suggestion Resistant AI offers is to switch from being a defender and turn into a hunter. It stated that firms need to start taking a pre-emptive approach. Within AML, pre-emptive risk management refers to proactively identifying and seeking out financial crime threats.

As AI-based AML solutions continue to rise, it shows a reactive, rules-based approach that relies on historical data and guidance from law enforcement is not sufficient. Regulators are also seeing the need for pre-emptive risk management, with this often featuring in new updates. Resistant AI stated that regulators now expect banks to use vast quantities of data at their disposal and available RegTechs to identify new risks.

The most effective and efficient way to become pre-emptive with AML is through AI technology. This sentiment was also echoed by the Financial Action Task Force in a recent report.

It stated, “Artificial intelligence (AI) and machine learning (ML) technology based solutions… can strengthen ongoing monitoring and reporting of suspicious transactions. These solutions can automatically monitor, process, and analyse suspicious transactions and other illicit activity, distinguishing it from normal activity in real-time, whilst reducing the need for initial, front-line human review.”

There are RegTech tools that are specifically designed to proactively detect financial crime. Not only do these protect a company from becoming a victim to fraud, but also helps them minimise how many customers are falsely flagged due to outdated systems and data.

While there are many providers of AI-powered solutions, there is a lot of buzzwords and information need to sift through to understand the benefits AI can have on their operations. The new report aims to give firms clearer answers.

Read the full whitepaper here.

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