tamerlane martyn

Specialist software and services provider Alfa has published a new whitepaper that reveals how asset finance companies can launch their own machine learning projects to enhance efficiency and customer service.

The paper is the second in a series of reports on the issue and focuses on real-world examples of how to successfully introduce the technology.

Called ‘Using Machine Learning in the Wild’, it provides two detailed case studies of companies that take different approaches to machine learning implementation.

The first example takes an off-the-shelf approach to training machine learning models, drawing heavily on standard tools.

The second 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.

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 whitepaper features a foreword by Blaise Thomson, whose speech technology start-up VocalIQ was acquired by Apple and formed an important part of the Siri development team.

Using Machine Learning in the Wild is the second in a series of whitepapers on the issue of machine learning. The first, called ‘Balancing Risk and Reward’, was published last year and outlined the high-risk, high-reward nature of using artificial intelligence in asset finance, including machine learning.

Alfa is the provider of Alfa Systems, a leading-edge technology platform for equipment and automotive finance companies, serving more than 30 clients in 26 countries.

To download the whitepaper, complete the form below.

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