I’ve participated in several standards bodies. The standard is often delayed due to competition driven by different use cases and different business market realities. This article suggests an in-memory standard for AI is the best approach.
I would bet there are others that will argue for a different approach, perhaps leveraging existing streaming data analytics or specialized hardware platforms. Currently, all of these approaches are being deployed and I doubt a traditional standards approach could work.
Breakthroughs in AI are being discovered weekly. It is more likely key suppliers will bring their solutions to market as open source and platform providers will produce specialized systems to address specific use cases. That said, the article does identify several key areas that need to be properly managed for an AI solution to succeed:
“Deploying AI for bespoke services demands the writing of tight, effective production-ready code, especially for the use of AI in fraud detection, which must happen in real-time and have a low occurrence of false positives. AI is still developing in this regard – the code and tools used by data scientists often require extensive customisation to become useful to enterprise developers and must be specifically modified to run at scale and in real-time.
AI works best when it has access to a large amount of compute power and high data bandwidth.
The squeeze to develop these low false-positive models means they’re often developed by data scientists, many of whom rely on retrieving data from disk, rather than from main memory. This disrupts developers’ attempts to orchestrate actual inference in real-time, as the seek time when searching for data is too long. Some tools are catching up, though, and inference is beginning to be treated as a real cog in the machine of enterprise software.
Overall, demand is growing for a more standardised approach that pulls and processes a variety of data sets simultaneously. Once the industry adopts this maturity in inference and opts for in-memory databases, AI’s use in fraud detection will become more widespread.”
Overview
by Tim Sloane, VP, Payments Innovation at Mercator
Advisory Group
