How do you prepare regulations for machine learning?

The journey toward making financial regulations machine-readable is a complex but necessary process. The Financial Conduct Authority (FCA) initiated efforts to convert parts of its rule book into a machine-readable format, piloting this with several banks. Unfortunately, these efforts were paused, highlighting the challenges of transforming legal language into something that can be easily interpreted by machines.

A key aspect of this transformation involves creating API interfaces, allowing for seamless data exchange. This approach is crucial for making regulatory information more accessible and actionable. A notable example is the U.S. Securities and Exchange Commission (SEC) mandating that large corporations and banks submit sustainability reports in XBRL format, which automates and simplifies reporting processes.

This shift towards machine-readable formats, like XBRL, not only enhances regulatory compliance but also improves the efficiency and accuracy of financial reporting, setting a precedent for future advancements in the industry.