Alfa, provider of the asset finance software platform Alfa Systems, recently published the third and final paper in its thought leadership series, “AI in Equipment and Auto Finance.”

The whitepaper titled, “Part 3: Moving Forward with Machine Learning” was produced in association with Alfa iQ, which is a partnership between Alfa and Bitfount.

Bitfount works with some of the world’s leading auto and equipment finance companies to build and deploy machine learning models.

Combining the theoretical insights from Alfa’s position paper, “Balancing Risk and Reward” with the use cases explored in the technical paper, “Using Machine Learning in the Wild,” the company highlighted the new white paper explores the trajectory of machine learning, its uses in auto and equipment finance, and how machine learning will continue to advance in the near future.

The project also includes in-depth exploration of federated learning and how organizations can use private data to train machine-learning models without ever compromising the privacy of that data.

Martyn Tamerlane, author of “Moving Forward” and a solution architect at Alfa said: “AI and ML represent an exciting shift for finance providers and, while the benefits are better understood now than they were a couple of years ago, the practical side to acquiring those benefits is still unclear for many.

“Alfa’s aim for this series has been to expose that practical side; to demonstrate where ML can help solve problems and make lenders more competitive, through its ability to detect patterns in vast amounts of data and feed that into higher-quality, sometimes fully automated, decision making. Then, to show ML taking different forms; first as an in-house framework, and secondly relying on AI-as-a-Service,” Tamerlane continued.

“Now we consider ML's continued success, particularly in the context of the ever-increasing volume and variety of data that is being collected; but with complex challenges posed by data privacy, fairness and the high level of expertise required to analyze the data effectively. By illuminating the key characteristics of this technology, we’re providing a platform from which people can effect major change,” Tamerlane went on to say.

The series can be downloaded at