Welcome to the Paragon resources hub
Here you’ll find a collection of useful materials on the techniques and considerations when it comes to developing, implementing, using and managing credit risk models, as well as the latest Paragon opinion and industry developments.
Machine Learning: Five guiding principles in credit risk modelling
The interest in using Machine Learning (ML) for credit risk modelling continues apace. In this post, we explore what’s driving this and five ways to make adopting ML models a reality.
Using open banking data throughout the credit lifecycle
How can open banking data enhance or even transform credit risk management, from application processing to credit management, debt recovery and fraud risk?
Data selection bias - can it be a matter of life or death?
Data gets used to create models, that get used to make decisions, such as whether an application is accepted or rejected, how much someone can borrow, for how long and at what price. Or what to sell, which transactions to authorise, whether to increase or decrease limits, how and when to chase payments and more.
With bad decisions having a profound effect on lender and customer, how does data selection bias impact the modelling that drives those decisions?
Paragon: the credit expert’s choice
“We get value from Modeller due to its efficiency, time saving and auditing features.”
CARLIEN KRUGER, SENIOR MODELLER, WESTPAC
Find out why credit risk analytics experts choose Paragon software.
Our software
Whether you’re building and deploying models, automating decisions or managing model risk and governance, Paragon’s software comes with our no compromise, valued engineering built in.