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The discussion, which you can catch-up on in full here, focussed on the data that underpins these models.

Here are my key takeaways:

1. Your models are only as good as your data.

The speakers stressed the need to tailor models to the specific characteristics of the data and agreed that the effectiveness of machine learning depends on the complexity and nuance of the dataset.

For simpler, more traditional portfolios, machine learning modelling might not be necessary. As Joe states, "If you're not bringing in any new data, and if there's nothing new you need to learn, then why do we need it?"

For more complex data sets, commonly known as "big data", automation is "almost essential" says Steve, as you need the support of machine learning or AI technology to be able to drive, extract and analyse useful pieces of information.

Data provenance was a hot topic of the webinar, with improper ownership of data potentially leading to severe consequences for organisations. Relying on data without proper ownership can pose legal risks and thorough examinations of contractual agreements are an absolute must.

2. Your models aren't necessarily biased, but your data might be.

As Joe stated, "The data we get reflects the world we live in, and I don't know any societies on Earth that don't have bias."

In the past, linear models and bureau data were often given a "free pass" with the assumption that biases were acknowledged and accepted. According to Steve, "We have accepted bias in credit scoring systems and other similar systems built on regression methods and there is this well-established view that that almost doesn't matter”.

However, with the introduction of machine learning and alternative data, the burden falls on financial institutions to prove model impartiality. Steve compares this to self-driving cars: "When humans drive cars, we accept a certain level of accidents, but as soon as you get into the self-driving vehicle world, they have to be almost perfect. To demonstrate that they are as safe as humans isn't sufficient”.

Steve continued "We're now using those machine learning algorithms to bring in data sources that we would never, or are very unlikely to have used, in regression methods. So, the bias looks like it's worse, but it's actually the new data."

Steve shared his perspective on addressing bias: beginning with data assessment before model creation. He suggests a shift from post-model assessment to pre-model scrutiny, emphasising the importance of thoroughly evaluating data sources for potential biases and dangers. Implementing explainable AI or machine learning models is also highly beneficial here as they provide transparency to the modeller and allow them to investigate why the model has made certain assessments and decisions.