There's a new subset of artificial intelligence/machine learning (ML) that are taking over news feeds called Generative AI aka Gen AI. As compared to where traditional ML model output predicts, classify or cluster, Gen AI as the name suggests aims to create new content like text, video, audio, image, or code from the training data.
Encouraged by regulators around the world including FinCEN in the US, Financial Conduct Authority in the UK etc financial crimes compliance industry has been in forefront to adapt innovative technologies to catch bad actors and organized criminal network. In a recent press release European Union said - "Generative AI systems and virtual worlds are disruptive technologies with great potential." (source).
The success and maturity of financial crimes compliance programs across the globe has been a journey. Even after decades of public-private partnership, there are areas of amendment opportunities whether it is client risk assessment, suspicious transactions monitoring, case investigation, documentation regular reporting, and model management.
Below are some potential areas of application for GenAI in financial crime compliance.
There are other areas where GenAI or LLLM specifically could be applied though, above are some key once with possible highest impact.
Below limitations and a robust plan for manage the same should be factored part of the initiative.
In addition to some of the known limitations outlined above, Gen AI may be prone to problems yet to be discovered or not fully understood.
Considering the freshness of GenAI the first step is to pick the right technology & business partners for the initiatives. A technology partner that brings both domain experience, data scientist skills and necessary infrastructure to manage the data, train, and deploy these models. Financial crime compliance domain expertise is key to ensure institution-specific business needs can be translated efficiently from the technology standpoint.
Furthermore, the right partners it is crucial to map a short and long-term roadmap for the adoption. Short-term roadmap should align with institutions' specific business requirements and gaps, and success criteria to assess to effectiveness of those use cases.
For timely engagement from IT, business, model management and other stakeholders, senior management buy-in and investment should be agreed upon. Regulators are key stakeholders especially for complex initiatives like these once thus, keeping them loop and updating them about the roadmap, risks, and plan to mitigate those risks is must.
Finally, due to high data and compute capacity requirements, the technology needs for this initiative are expected extensive, driving higher costs. Therefore, aligning business value and benefits with investments with proper timelines of return on investments (ROI) is crucial for the scalability of the initiative enterprise wide.
It is estimated that, Gen AI could enable automation of up to 70 percent of business activities, across almost all occupations, between now and 2030 (source). Considering it is an evolving area the knowns limitations and risks should be well factored. Good news is that financial crimes compliance industry is be starting from scratch as there's quite a lot of learning from AI/ML work done so far. As the interest grows the investment will rise in the technology, skill set etc. Partnerships between technology vendors, institutions, consulting firms, and regulators are key to leveraging the value of Gen AI.
