AI-optimized compliance – not just for unicorns

Fintechs with smaller datasets can power-up their compliance processes with AI-driven solutions.

Big financial players have been investing heavily to deploy artificial intelligence (AI) in their anti-money laundering systems. From megabanks to fintech unicorns, compliance departments are increasingly taking advantage of machine learning (ML) and big data to improve their ability to detect financial crime.

Meanwhile, smaller fintechs and financial institutions struggle to implement similar anti-money laundering programs, despite often having a strong AI capability within their core product. This is mostly because AI in the risk and compliance domain requires not just technical expertise, but also significant domain knowledge.

The assumption is that bigger is always better for AI and ML tools – and that’s true in most cases. As the amount of data increases, so does the ability to recognize criminal patterns and develop insights. However, the size of your dataset doesn’t have to be a limiting factor when using AI/ML to optimize compliance processes. Focus on the data that you do have, rather than the data you don’t. Focus on the behavior of your good customers, where you have far more datapoints.

Custom models

Smaller fintechs often rely on off-the-shelf models or rules, that will never produce the best results for everyone. This may seem like the only cost-effective option for these players, but there’s a better way.

Even smaller fintechs can generate custom models on their data. By modeling the behavior of their good customers, automated model generation can help to recognize the relevant patterns even on their smaller datasets where there are fewer anomalies that indicate financial crime.

Size matters

While this kind of approach works well at any scale, it’s particularly effective for smaller teams that don’t have the resources to manage a complex solution.

Improving compliance and transaction monitoring processes at very large financial institutions can be a daunting task that may take years to fully complete, but for smaller players it can be done much more quickly.

In practice, it’s much easier for fintechs and smaller financial institutions to optimize their compliance processes. Smaller datasets, fewer legacy environments, and flatter management hierarchies tend to mean they are much more nimble organizations. And they want to maintain that agility.

Getting started with AI-optimized compliance

At Sygno, we’re building Automated Model Generation for regional and cooperative fintechs and smaller financial institutions, based on the patterns of their good customers. Which luckily is the majority of their data.