BIRMINGHAM, Mich. -

Alfa acknowledged that some auto-finance companies still have questions about what artificial intelligence can do for their operation.

To offer some potential answers, the provider of Alfa Systems, a technology platform for equipment and auto-finance companies, recently released its second whitepaper about artificial intelligence in the industry.

The firm highlighted that its new whitepaper titled, Part 2: Using Machine Learning in the Wild, is a more technical follow-up to Part 1: Balancing Risk and Reward, which Alfa released last year.

The second segment explores in detail two specific use cases that take 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 center 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,” Tamerlane continued.

The first example, which addresses automated license 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.

Alfa is offering its material on this website.