AML effectiveness; A proactive approach…

Blog | 19 Feb 2021

Today, almost every major financial institution is navigating a huge transformation programme aimed at improving their anti-money laundering (AML) regime. Whether they are responding to regulatory pressure or to a new threat, they all face the same question… ‘How effective are we at catching financial crime?’

Here at SymphonyAI NetReveal Applied Intelligence, we recently commissioned a research project with Aite Group to interview AML executives on this question. The Impact Report gets inside the heads of AML compliance leaders to better understand their priorities and how emerging AML tools and technologies can best support them – and the results are fascinating.

“As AML compliance leaders around the globe work tirelessly to combat financial crime and achieve regulatory compliance, these efforts are complicated by a rapidly evolving landscape. To overcome these challenges, AML compliance leaders are embracing the enormous upside in big data and technology. But without transparency, explainability, and constant vigilance and fine-tuning, AML transaction monitoring models may fail to deliver on their promise and purpose, and can often lead to inadequate, and perhaps incorrect, intelligence, decision-making, and outcomes.” – Impact Report.

The findings make it clear that optimising AML models is a continual, time consuming and difficult process for a financial institution today. Multiple teams in the AML function have to go back and forth to understand new threats, create appropriate test data sets, and run tests to understand whether a particular risk is addressed. Often, AML investigators spend considerable time analysing test results and drawing conclusions on the effectiveness of AML models.

“Collaborating with SymphonyAI NetReveal, Aite Group probed how AML leaders are continually seeking increased intelligence in combatting financial crime and protecting their organisations and customers. Financial crime typology libraries and simulated data sets present appealing options in sharpening risk identification, building better AML detection models, introducing greater program effectiveness, and elevating operational efficiency.” Charles Subrt, Senior Analyst, Fraud & AML at Aite Group.

We know that “regulators are pressing for the increased use of innovation in elevating financial crime detection, while concurrently expecting transparency and explainability of AML models” . This is the key driver behind the new FinCrime Testing Service, created by the SymphonyAI NetReveal Futures Team.

The FinCrime Testing Service simulates financial crime typologies into data and uses that data to provide an independent test case of AML models. In doing so, the FinCrime Testing Service supports financial institutions with their AML risk management process by providing them with threat intelligence, test data and a meaningful way to quantify AML effectiveness.

Using the FinCrime Testing Service, a financial institution can start to tackle some of the key challenges raised in the AITE Impact Report and finally be able to face the critical question of ‘how effective are we at catching financial crime?’

Download the AITE Group AML Impact Report or contact us to learn more.

You can also access the report from the AITE Group website.