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July 2, 2021updated 13 Jul 2021 3:01pm

Alfa publishes third AI paper ‘Moving Forward with Machine Learning’

By Alejandro Gonzalez

Alfa, the provider of Alfa Systems, a software platform for the asset finance sector, has published the third and final paper in its series AI in Equipment and Auto Finance.

Part 3: Moving Forward with Machine Learning is produced in association with Alfa iQ, a partnership between Alfa and Bitfount that works with auto and equipment finance companies to build and deploy machine learning models.

The document explores the trajectory of machine learning, its uses in auto and equipment finance, and how ML will continue to advance in the near future. It includes an in-depth exploration of federated learning and how organisations can use private data to train ML models without ever compromising the privacy of that data.

Alfa & AI

Martyn Tamerlane, the author of the report, 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. 

“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 analyse the data effectively.

“By illuminating the key characteristics of this technology, we’re providing a platform from which people can effect major change,” Tamerlane said.

Alfa’s previous publications in the series are Balancing Risk and Reward and Using Machine Learning in the Wild.

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