Understanding and explaining the behaviour of machine learning-based models is rapidly becoming a top priority for companies in many industries and their respective regulators. In case of the heavily regulated financial industry, the quality of these algorithms can potentially impact the quality of life of millions of end-customers.

How are you doing it today?

Many companies are using machine learning and the speed of adoption is likely to increase. Hence, a proper framework for validation and understanding what the algorithms are doing must be implemented.

The main risks involved in using machine learning models are:

  • bias and ethical concerns as model outputs are open to bias inherent in training data;
  • issues around data dependency since a model may not be able to adapt quickly to abrupt changes or regime switches in the data generating process;
  • difficult to understand as complex non-linear algorithms with feedback loops make it hard to build intuition about how the model arrives at its output; and
  • need for constant monitoring as static controls are not mitigating compliance risks involved when using constantly changing self-learning algorithms.

Some companies are already starting to implement processes for independent validation of the models they develop in response to regulatory scrutiny. This includes increased use of sensitivity analysis and challenger models built with more explainable techniques in a bid to help overcome these challenges.

Who does it apply to?

Any company that uses models based on machine learning or other AI-algorithms for making business decisions should have a framework for independent validation in place.

Why is this important?

Establishing reliable validation processes for machine learning algorithms many industries can boost the use of machine learning and artificial intelligence in general. This would enable companies to provide vastly improved services to their customers and leverage an increased cost efficiency of their operations. Installing a more robust framework and tools to improve transparency and explainability will mitigate the risks and help resolve the challenges described above.

How can we help?

If you would like to know more about the challenges and opportunities involved in using machine learning algorithms and how Kidbrooke can assist you in validating your models, please contact us at info@kidbrooke.com and we will get in touch to arrange a meeting.

And if you are still researching this topic and want some help and insights on the way, please download our guide to machine learning model validation here or by clicking the button below. Please note that downloading the guide will require you to share your e-mail address with us. By downloading the guide you agree to the terms of our privacy policy.