What role does AI play in fraud detection & AML?
For financial companies, identifying fraud and managing compliance processes are key tasks that must be performed efficiently and effectively. Fortunately, artificial intelligence (AI) systems have the potential to make these tasks easier, cheaper, and better.
Financial organizations are increasingly integrating AI solutions into both new and existing workflows to strengthen fraud prevention and improve risk management. In fact, according to a recent survey conducted by KPMG, 76 % of financial institutions cite fraud detection as their top use case for generative AI.
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How is AI transforming fraud detection?
Fraud detection involves classifying transactions into those that are genuine and those that are fraudulent. Machine learning is well suited to this type of task – a problem of classification where there is a large amount of unstructured data as an input and a clear distinction between possible outputs.
Well-designed ML-based systems can detect subtle or hidden changes in user behavior or simultaneously look at multiple areas when a transaction fails to reach any single threshold for being potentially fraudulent.
By incorporating reinforcement learning (RL) techniques, the system can also continually learn and update itself without manual intervention and can handle a much larger and less-defined set of input data.
Applying AI in anti-money laundering operations
AI and ML can improve fraud detection systems, making them more efficient and reducing costs. Compliance issues such as anti-money laundering (AML), however, pose a different set of challenges.
Whereas transactional fraud is a financial problem for card issuers and merchants, AML is a regulatory problem and getting it wrong can lead to fines and penalties many times larger than the size of the transaction, as well as the risk of criminal penalties and reputational damage.
Fraud detection vs AML
In many ways, fraud detection is a simpler problem than AML.
Each fraudulent transaction is – generally – for a relatively small amount, and the penalty for missing it is, at most, the value of the transaction. A false positive is easily resolved at the cost of a minor inconvenience to a client, who must confirm the validity of the transaction, perhaps using an app or phone call. A false negative carries a known cost (the value of the transaction) and a false positive is cheap and easy to resolve.
Given this, fraud detection ML systems can focus on simply identifying potentially problematic transactions as quickly, thoroughly, and cheaply as possible – and there is usually plenty of data available to make the process easy.
In contrast, AML problems carry potentially huge penalties for false negatives (failing to notify users of potential problems). Employees can face civil and criminal penalties, institutional reputation can take a hit, and the fines can be sizeable. Therefore, AML systems are usually designed to be overly cautious in identifying potential problems – to raise the alarm at any hint of wrongdoing.
To achieve this, there are traditional, rules-based AML systems that issue alerts, such as notifying a user when clients are Politically Exposed Persons (PEPs), when transactions cross borders, or when transactions seem to have no business purpose. Because the risks associated with false negatives are so high, AML systems tend to err on the side of caution and raise the alarm whenever there is a chance of a problem.
However, it is estimated that only 1 to 2% of such alerts become a Declaration of Suspicion (DS) – a transaction that is considered worthy of further investigation. In other words, AML systems produce many false positives. This is costly, as compliance teams or managers must investigate each alarm. Therefore, a key goal for AML AI systems is to reduce false positives while avoiding false negatives. ML can add enormous value to such systems by making it easier to identify false positives and by making the system more effective at identifying real issues.
The content for this article is taken directly from Intuition Know-How‘s tutorial ‘Fraud & Compliance’ taken from the ‘AI Applications’ course which is part of Intuition Know-How’s comprehensive AI & Fintech channel.
Really insightful breakdown 👏. What stood out to me is the contrast between fraud detection and AML — one being about efficiency and speed, while the other is about caution and avoiding severe regulatory risks. The challenge of reducing false positives without missing critical red flags feels like the sweet spot where AI + ML can truly transform compliance. Especially reinforcement learning, which allows systems to keep learning without manual intervention, seems like a game-changer. Excited to see how these technologies will balance both risk management and customer experience going forward.