The economic cost of money laundering in the US is staggering – in 2021 alone, the median loss of money laundering offenses in the US was approximately $293,359 – and banks are bearing the brunt of this financially and reputationally. However, the problem is more than just financial, it is a human one. Laundered funds are being used to carry out illicit activities, of which, the State Department predicts there are more than 27 million people are victims of human trafficking. As recently as July 2023, the US Securities and Exchange Committee issued a risk alert to brokerages citing deficiencies in AML controls.
Banks have a lot to balance. Achieving a robust client risk-rating methodology and an effective suspicious activity monitoring and reporting program, along with staying ahead of evolving regulatory requirements, are just the start of many challenges they face. Compounding matters is the complexity of identifying and tackling the inherent blind spots inside and outside the organisation.
How can organisations stay ahead of the bad guys in the fight against financial crime?
AI alone is not the answer
While artificial intelligence helps bring down false positives in financial crime risk management extensively, it can’t be seen as the silver bullet. Complementing it with a low/no-code system, where scenarios can be meticulously crafted and assessed, remains indispensable. Moreover, the cornerstone of any AI-driven approach should be explainability and traceability.
Read more on the need for explainability of AI in AML
A risk-based approach, coupled with the power of AI in client screening can empower compliance teams to focus their resources and take enhanced measures in situations where the risks are higher, apply simplified measures where the risks are lower and exempt low-risk activities. This way, they can redirect their time and efforts effectively, to stay one step ahead of criminals.
Additionally, for AI to work effectively, the right data needs to be fed into the system.
Dive into the data
Data is key to an effective client screening strategy. Banks must analyse and organise data before plugging it into the system. The best system is useless without good data to maximise AI-driven solutions. Analysing the state of data to identify pain points and gaps in compliance processes is paramount as they vary from client to client; excessive matches, high false positives and negatives, unnecessary data overload (both internal and external), or applying actions meant for high-risk clients to those with low-risk profiles, to name a few.
A comprehensive data strategy requires understanding the importance of quality external data, as well as consistency and formatting of internal data. The insights and alerts a financial crime compliance solution produce is only as good as the data it is fed; better the data, closer compliance professionals are in minimising risks.
Read more on empowering compliance through the right data strategy
Knowledge is power
It is never a one size fits all solution when it comes to financial crime compliance. The external environment is constantly changing – criminals are finding new ways to evade detection with evolving typologies and new technology.
Instead of starting with technology, compliance professionals should audit and analyse their own specific use cases, controls and various scenarios according to their organisation’s risk policies. This followed by new rule sets and multiple screening configuration trials to self-build and test rules in no-code environments, using real data in a sandbox can empower compliance professionals to test and adapt their rule sets in accordance with changing external factors.
Embedded flexibility and multi-configuration set up will enable banks to future proofing "the unknown" by proactively managing evolving typologies. The right skills coupled with scalable technology that grows with the business, is the key to always staying ahead of the bad guys.
The process of screening customers, vendors and transaction data against politically exposed persons and sanctioned entities lists is a critical regulatory requirement for banks. Read Napier’s eBook on how to reduce false positives through sanctions screening
