Why you should read the European Banking Authority report on AI and Big Data
You might have missed this highly informative report from the European Banking Authority (EBA) – because the title didn’t contain the popular buzzwords of Artificial Intelligence – AI or Machine Learning – ML (nor does the front cover have a picture of a robot!). But for anyone who is trying to understand the challenges ahead for AI and broader data management in banking I think this report provides a rare unbiased, concise and highly educational deep dive into pretty much all of the key topics involved.
I won’t give a synopsis here, just some reasons why I think you should read it:
- It’s really all about AI in Banking!
‘Advanced Analytics’ is the term the authors use for AI, ML tech.
- BS Free
Provides most of the background you need to see through the smoke, mirrors and hype surrounding AI or Advanced Analytics.
- It’s a great introduction
But not dumbed down – Great for business people who need a better understand the challenges their data scientists and AI professionals face, and great for data scientists who need to understand the broader applications and implications of this rapidly emerging technology in Banking. If you don’t know what kind of algorithm might be used for a particular business case this is for you. If you are trying to understand what a data scientist means by accuracy and a confusion matrix this is for you too.
- Technologically Neutral
The report maintains technological neutrality and with so much information these days coming from vendors of proprietary tech, in a world where there are few common open standards, it’s hard to find information that doesn’t in someway imply vendor lock-in.
This report cover’s pretty much everything including Data Quality, different types of ML, explainability and interpretability, ethics… So many reports are very narrow focusing on one use case or tech, but this takes the whole horizon into account.
It describes practical use cases for AI and the technology involved – I was particularly impressed with the technical content: accurate concise and easy to understand. More importantly, it also describes all the potential problems – things like: how automated credit scoring could be ‘gamed’ by an institution’s sales staff and could coach uncreditworthy customers on how to be granted a loan!
- Forward thinking
It covers the topics of ethics in AI and even security in AI. Ethics has obviously been talked about a lot in recent months (sometimes with slightly fanciful references to Asimov’s laws of Robotics!) but this report lays out some really good practical steps that need to be implemented to ensure ML solutions are fair. It’s also refreshing to see serious consideration to security (data poisoning, adversarial attacks, model stealing) something I blogged about a couple of years ago – (https://www.linkedin.com/pulse/threat-poisoned-data-ai-alex-brown/). It’s a bit like in the old days of software development when people didn’t really take things like SQL injection or cross site scripting seriously, resulting in security breaches in many applications and web sites. If AI solutions aren’t built with security from the ground up, the next few years could see echoes of these past security breaches played out in the AI domain.
You can get the report here:
Alex Brown is Datactics Chief Technology Officer. He is a former Head of DART Development at Vela (formerly SR Labs) and Market Data Technical Consultant at NYSE Euronext and has over 15 years’ experience in software development and technical innovation.
Press contact: Tania Ahmed
Blog Categories: CTO Vision.