Kyt intelligent risk engine professional#

In risk-based rule classification, rules are created in the traditional way: based on customer, transaction and/or account characteristics. In practice, the explainability output could include variables like ‘the number of cash withdrawals above €10,000 in the last six months’ or ‘the amount sent to high-risk AML countries in the last month’. These are able to identify which variables have been important for the model to activate the trigger. Explaining why an investigation into a customer was triggered (or not) is not as straightforward as in the traditional approach, but thankfully, methods are available to add ‘explainability’ to AI models. Of course, as a new way of working in a highly regulated context, the use of AI requires a mindset change. This allows them to improve triggers that were previously incorrect or recognise new types of money laundering that have been identified by police investigations, attentive colleagues or anomaly models. Plus, when supervised learning is applied, AI models learn from past investigations. This means less time spent on investigating false positives and more time spent on actually getting to the bottom of suspicious activity. Most importantly, they allow financial institutions to reduce the number of investigations required to find the same number of potential money launderers. Risk scoring – Matching similarities in data patterns with historical cases of money laundering (aka ‘supervised learning’).ĪI methods are brimming with benefits.Network analysis – Detecting interconnections between customers in a network of transactions.

Anomaly detection – Detecting behaviour that is atypical for the customer in question.As AI is a broad field of science, it takes many forms in the context of AML, such as: In recent years, the use of artificial intelligence (AI) in the detection of potential money laundering has skyrocketed.

Kyt intelligent risk engine professional#

On top of that, as money launderers becoming more and more professional and regulators are raising the bar in terms of AML investigation requirements, the number of rules needed to cover all potential money laundering cases is constantly increasing. But of course, the remaining 95% still need to be investigated, demanding time and effort from the AML team. In general, less than 5% of investigations lead to a customer being reported to the regulator. As soon as new types of money laundering are identified-in the news or in the portfolio-a new rule can be implemented to monitor these risks.īut what about the downsides? These rules also trigger investigations into customers that are not laundering money at all (aka ‘false positives’). The main benefit of using rule-based triggers for AML investigations is that it’s relatively easy to explain why a certain customer has been singled out for investigation. This approach has been common practice amongst financial institutions and regulators for years.

But they can also be quite complex, using combinations of input variables, or tactics like whitelisting (creating lists of persons or entities that should not be triggered) or other exclusion criteria. These might be relatively simple, for example, a rule that determines all cash withdrawals above €1,000 should be investigated. Traditionally, financial institutions have exposed AML cases by creating rules (or ‘scenarios’) that identify suspicious behaviour.