Here's a step-by-step explanation of how bootstrapping works in scorecard development.

Monitor the credit risk cycle to reduce losses and maintain a healthy credit portfolio with effective risk management measures.

In this blog, we will discuss the concept of reject inference and how it is used in scorecard development.

NZ faces rising uninsurable property risks from extreme weather. Happy Prime CEO urges action to prevent financial disaster for Kiwis.

Align objectives, actions, KPIs, and responsibilities with the X Matrix Planning tool for strategic credit risk management.

Happy Prime is cautioning about the potential drawbacks of the new DTI measures.

Information Value (IV) measures a variable's predictive power in credit risk scorecards, aiding robust and accurate credit scoring.

Explore how observation and performance windows enhance credit risk assessment accuracy and reliability in scorecard development.

Explore the essential role of application scorecards in credit risk management, focusing on accuracy and reliability.

What exactly is Loud Budgeting, and why might it be the financial buzzword of the year?

Why do banks find house lending far more appealing? In this blog, we aim to unravel the intricacies of this capital-driven strategy.

Happy Prime Consultancy is proud to join the global celebration of International Women's Day.

Discover practical advice for managing financial difficulties.

Explore the nuanced debate on Debt to Income vs. Uncommitted Monthly Income assessments in banking.

We look at the dangers associated with unintentional bias in application scorecards and its profound implications on fairness, equality,...

Discover how recovery curves can improve collection and write-off management, boost financial health, and enhance organisation's processes.

Markov Chains are a powerful mathematical framework that plays a crucial role in credit risk management.

We’re adding a sprinkle of financial wit to your festive season with some secrets to a credit-smart Christmas.

We explore what factors influence the choice of sample size and how to strike the right balance to ensure a robust and reliable method.