Published on in Financial ServicesInnovation

Maintaining compliance is tough. With financial regulation continuously evolving and varying across specific industries and geographies, it’s an ongoing struggle for organisations not only to keep up with change, but to ensure they’re compliant.

RegTech helps companies do this better.

But what exactly is RegTech?

The FinTech sector has long been an exciting world to be part of. With huge buzz and expectation, it’s attracted plenty of venture capital attention in recent years; in 2016, global investment grew 11 percent to $17.4 billion.

Operating as a specific area within the FinTech sector, RegTech applies similar principles – agile development methods, cloud operations and analytics – to the regulatory environment. Hence, as you might have already guessed, the name…

In general terms, it helps large financial institutions save money by improving productivity of compliance-based business processes and also saving money through enhanced fraud detection.

Artificial Intelligence (AI) and Machine Learning (ML)

At the heart of most RegTech solutions are automation, AI and ML (the application of AI). These technologies help with complex pattern matching across multiple structured and unstructured data sets, helping spot data anomalies that, for example, point to potential incidents of fraud.

At Fujitsu, we’ve developed the Zinrai AI platform, which is being used in financial services to enhance trend analysis.

AI can be applied to multiple data sets, including behaviour patterns in heterogeneous data sources (such as social media or stock market prices), to deliver rapid results.

Traditionally, systems have only searched individual data sets, but today it’s possible to feed in multiple sources, delivering new and often more valuable insights.

An example of ML in practice could be a financial services trading database containing hundreds of thousands of trading transactions per week. However, once this is analysed alongside data from social platforms it’s possible to understand if any specific behaviour on Facebook, for example, relates to these trades.

Going further, it’s even possible to join the data set to public news sites and stock market movements, revealing the ripple effect that can easily propagate from a trading anomaly.

Will existing solutions soon be obsolete?

While AI is a powerful tool, it can’t do everything – at least not yet! The truth is that the regulatory landscape requires a broad mix of knowledge and solutions to be properly navigated.

AI in the RegTech space is also still in its infancy. Organisations are experimenting and co-innovating to explore its applications and limitations.

As with any area of IT though, it has the potential to grow quickly as the market and technology matures.

The time is right to start thinking about how AI applied to RegTech can ease your regulatory burden, boosting operational efficiencies and helping identify the anomalies that could ultimately hurt your business.

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