It’s a cutting-edge technology which financial services and other organisations will already be familiar with but may not fully understand. The rise of Machine Learning has long-been discussed, promising a new way of delivering successful outcomes and solutions across a myriad of services and applications.

The future is already here but we are now reaching a critical juncture where businesses need to begin prioritising their Machine Learning capabilities – or risk being left behind as competitors embrace it and reap the benefits it can bring.

What is Machine Learning?

Machine Learning can appear complex but simply it can help businesses analyse and interpret vast volumes of data instantaneously, giving organisations the opportunity to make better, smarter and more accurate decisions. The more data which is fed into the system, the better the results will be.

Although Machine Learning has been widely used in areas such as fraud, companies are already beginning to incorporate it into their credit-risk decisioning processes. Some have already implemented it, while others are operating with a view to introducing it within the next 12 months.

In credit scoring, traditional scorecards are developed using numerous variables. During the pandemic many of these variables have changed drastically – including employment, income and spending, savings, and debt. Inevitably, credit scorecards developed pre-pandemic are no longer as predictive and accurate.

The need, therefore, to recalibrate scorecards post-pandemic is pressing, which is where Machine Learning comes in.

Traditional credit risk models use tens of characteristics to allocate points and develop a credit score for each customer. This tells the lender whether an individual is a good or bad credit risk based on how likely they are to default. Machine learning derived credit scoring models use hundreds of variables and a range of data to take a more complete view into a customer’s behaviour.

The benefits of Machine Learning

The benefits are clear: Machine Learning models outperform traditional approaches to credit risk. One UK-based lender saw an uplift of 12% in GINI, which is a measure of how accurate the model in identifying “good” and “bad” borrowers.  They were also able to increase their acceptance rate for new credit applications by 16% - with no increase in bad debt.

Moreover, this innovation is driving real benefits for customers. Thanks to more accurate affordability assessments, lenders can offer lending to those who previously wouldn’t have been accepted, widening customer bases and welcoming more people into the mainstream financial system.

Development of Machine Learning can also be rapid. Development time for a risk model can be as little as four to six weeks, compared to around 12 for a traditional, regression-based model. These models can then be deployed seamlessly into a decisioning platform. Models can also be tweaked and customised to ensure they’re up-to-date and reflect any changes in the industry and wider macro-economy.

The future of Machine Learning

The building and management of Machine Learning requires specialist skills and specialist software, while its incorporation into legacy infrastructure can also be an issue. Furthermore, as models become more and more complex, it’s critical for you to be able to understand – and explain – why you have reached the decision you have.

But those factors are not insurmountable. As we’ve seen, it is already bringing tangible benefits that would have been impossible to imagine even five years ago.

Those companies which are already reaping the benefits – or are planning on doing so – have stolen a march on their competitors. It’s not too late but there is a risk that those which don’t could be left behind at rapid pace. The time to act is now.