New AI Paper: "Using Machine Learning in the Wild"

AI in Equipment and Auto Finance - Part 2

AI in Equipment and Auto Finance - Part 2

Following 2019's ​"Balancing Risk and Reward", Alfa has released its second paper on artificial intelligence in the industry, with a foreword by Blaise Thomson.

Alfa has released its second paper on artificial intelligence in the industry.

Part 2: Using Machine Learning in the Wild is a more technical follow-up to 2019's Part 1: Balancing Risk and Reward, exploring in detail two specific use cases which take very different approaches to machine learning implementation. It features a foreword from Blaise Thomson, whose speech technology start-up VocalIQ was acquired by Apple and formed an important part of the Siri development team.

Martyn Tamerlane, a Solution Architect at Alfa and co-author of the paper, said: “AI and machine learning are front and centre in the asset finance conversation at the moment, but many don't know where to start - how much expertise they need, what they can outsource, and where they should concentrate their efforts and costs.

“Our worked-through examples convey genuinely useful and practically applicable advice for people wanting to kick off their own machine learning projects. By comparing the approaches used, we offer advice on what's right for others.”

The first example, which addresses automated licence plate recognition and its ongoing embedding in business processes, takes an off-the-shelf approach to training machine learning models, drawing heavily on tools provided by AWS. Meanwhile the second, which analyses Alfa's internal code tests, is carried out wholly in-house with existing resources and knowledge. The paper also features a decision aid to help readers clarify how their projects might compare. 

2019's Balancing Risk and Reward outlined the high-risk, high-reward nature of using AI in our industry, and machine learning in particular. Alfa will continue its commentary on AI in asset finance with further upcoming publications.

Read Using Machine Learning in the Wild now.