Credit risk is the probability that a customer won’t be able to make a required payment, causing a loss for the bank or financial institute that provided the loan. It's one of the most common risk type when it comes to the financial industry, which explains why companies and institutions try to make their credit risk evaluation as efficient as possible.
Most of the times, though, traditional risk models segment customers based on broad categories, for example new vs existing customers. While this can be helpful for an initial understanding of a customer’s trustworthiness, it doesn’t really cover how certain individuals might behave and it isn’t efficient in the long run.
However, in a world where big data offers analytics and predictive models like never before, it’s possible to offer an improved credit risk prediction system to banks and companies that require so.
By using scoring models that are AI-based and use deep learning, banks and financial institutions can access more realistic predictions on credit risk, using customers’ credit history and the power of big data. This way credit can be approved to the right people and better pricing options offered to people who deserve it.
Using Axyon’s platform, we gather the institution’s financial data, normalize it and use it to develop a prediction model with deep learning techniques. The client provides Axyon with a benchmark, made with their traditional statistical model: the benchmark represents the performance range based on the AR metric, which evaluates the accuracy of the system by comparing true positive and false positive percentages.
The development of deep learning models, evolved and trained using our technology, allows for a sensible improvement of the benchmark’s performances, resulting in a more precise default probability prediction.