Is AI a must to deal with today’s data?
We enjoyed the food for thought offered in a recent opinion piece by Jo Ann Barefoot on the case for placing artificial intelligence (AI) at the heart of digitally robust financial regulation. She posits that “until only recently, there wasn’t enough data in digitized form—formatted as computer-readable code—to justify using AI. Today, there is so much data that not only can we use AI, but in many fields like financial regulation we have to use AI simply to keep up.”
The article discusses use cases where regulators could get more from underutilised data, such as anti-money laundering, fraud prevention, credit discrimination and predatory lending, and understanding climate-related risks. It also examines some of the challenges, including bias, data protection and data quality – noting that structured data (such as that created by tagging documents using XBRL) is easiest for AI to use for meaningful, high-quality outputs.
“Digitization of data can solve some problems and cause others,” it concludes. “The key to achieving optimal outcomes is to use both data and AI in thoughtful ways—carefully designing new systems to prevent harm, while seizing on AI’s ability to analyze volumes of information that would overwhelm traditional methods of analysis.”
Read more here.
