• Balancing Risk and Reward:

    AI in Equipment and Auto Finance

  • esg

  • Investors

  • Contact



Balancing Risk and Reward:

AI in Equipment and Auto Finance

As AI becomes more widespread in equipment and auto finance, many providers are making sure they won't be left behind.

Part 1 of our series on AI in equipment and auto finance, Balancing Risk and Reward, explores the reasons why AI can provide solutions that are superior to those which use conventional methods. We discuss various industry-specific use cases, and how AI solutions can offer not just great reward, but also great risk. Finally, we share the ways we innovate using AI here at Alfa.

See also Part 2: Using Machine Learning in the Wild and Part 3: Moving Forward with Machine Learning.

  • Why AI?

    AI has been found to be highly suited to many subject areas where forecasting and pattern recognition is profitable, or facilitates cost reduction.

  • Why AI?

    With an explosion of data and computing resources available to us, alongside mature machine learning methods, it has become cheaper to experiment with solutions and, ultimately, productionise them.

  • AI-as-a-Service

    AI-as-a-service reduces up-front cost and allows you to concentrate on the parts of the process that form the core of the solution.

  • AI-as-a-Service

    AI-as-a-service has removed the need for an intimate understanding of how machine learning works. As a result, large-scale projects are no longer required for AI-enabled business impact to be transformative.

Training a neural network.

  • stats

    Get Data

    Obtain training data, containing a large number of individual input data along with the correct outcome.

  • stats

    Clean, Prepare and Manipulate Data

    Pre-process the training data. Typically, this involves someone with intimate knowledge of the data removing anything that is outlying or irrelevant. In some cases, a data scientist is required.

  • stats

    Train Model

    As training data is fed into the neural network, the accuracy of the outcome is fed back into it, tweaking its ability to get a more accurate outcome for the next part of training data.

  • stats

    Test Data

    Once all of the training data has been fed through the neural network, it is tested against some unseen data with known outcomes, to measure its accuracy.

  • stats


    The neural network is ready to process new data. It is unlikely to be 100% accurate, but responses to its outcome can be fed back automatically so that it adapts to new patterns.

  • Staying on top of...context

    Human judgement is vital to the successful configuration of the machine learning process.

  • Staying on top of...bias

    While there are methods to remove bias from training data and results, it's far from an exact science and some bias will inevitably remain.

  • Staying on top of...regulation

    Not paying attention to the data being used in an AI solution can lead to severe regulatory consequences.

Download the position paper

  • Ai Cover 2

    AI in Equipment and Auto Finance - Part One: Balancing Risk and Reward

  • AI in equipment and auto finance

    AI is no silver bullet; its design often requires precise expertise to guide solutions so they don't incur spiralling costs, or result in solutions that are less accurate than the processes they are designed to replace.

  • AI at Alfa

    At Alfa, innovation is key to our culture. It is embedded in the way we think about solutions. Our innovation culture isn't just philosophical; it extends to a hard budget too.

  • AI in equipment and auto finance

    There is a huge amount of value to be gained through AI, but only with the right judgement and guidance. The balance must be optimised to align with a business's strategy, while ethical and regulatory risks must also be considered.

  • AI at Alfa

    We take each valid idea forward using measured, iterative experimentation until it's ready to present and, finally, implement.

  • AI in equipment and auto finance

    In the vast majority of cases, AI is an estimation machine and is therefore most appropriate in scenarios where the full extent of rules or data is not known.