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.
The Paragon Best Paper Award 2023
We had great pleasure in sponsoring the Gala dinner at the recent 18th Credit Scoring and Control Conference held in Edinburgh and awarding the Paragon Best Paper Award. The biannual conference is organized by the Credit Research Centre at the University of Edinburgh and is the world's premier conference for credit scoring and credit risk related topics.
Paragon releases Modeller 5.0
Our latest release of Modeller, version 5.0, is now available to all new and existing users. We are really excited about the advances brought to our users. The major addition is the “Open source node”. This enables users to run Python scripts and packages directly within Modeller, enabling greater flexibility, efficiencies and ease of use along with the vast amount of choice and data science capabilities that Python offers.
Do you have the right tools for the job?
A recent McKinsey report highlighted some of the challenges that organisations are facing as they manage their model portfolios.
Predictive modelling – are your models degrading and under-performing because of data pitfalls?
Model Risk Management - focus on the benefits not just compliance
Model Risk Management (MRM) is the practice of identifying and mitigating the potential of model error or wrongful model usage. Where models are used by organisations to make or inform decisions, many industry regulators are demanding effective MRM practices to protect organisations, and their customers, prospects and partners.
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.
What do credit risk modelling and F1 have in common?
A light-hearted look at how F1 factors such as fuel mix, tyres, cockpit information, reliability, driver skills and safety compare with assessing whether to use machine learning or traditional logistic regression models in credit risk.
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.