Streamlined Credit Risk Modelling: How Modeller Empowers In-House Teams With Enhanced Governance And Compliance

(fourth article in a series of four) 

It is imperative for companies to have a robust and effective credit risk modelling process in place. Credit risk models (and many other model types) must pass regulatory scrutiny and internal audit requirements, all with the aim of ensuring that the models developed will perform as required and to minimise any potential model risks. The accuracy and reliability of this process are crucial for financial institutions, as their credit risk models directly impact their lending decisions and overall profitability.

This can be a daunting task for in-house teams, as it requires strict governance and compliance measures to ensure accuracy and repeatability. This is where Paragon’s Modeller comes in – an innovative tool that empowers in-house teams by providing a standardised process and enhanced governance features. In this article, we will discuss the importance of having a standardised process and complying with regulations from regulatory bodies across the world, as well as the challenges that come with using tools that are less structured.  Recent regulatory publications and updates to modelling regulations include those set out by the European Union IRB model requirements, US SR 11-7 guidance, the Bank of England PRA in SS1/23, and similarly in the UAE with the Central Bank model management standards and guidance. 

Define and Use Standardised Processes Without Losing Creativity  

Having standardised processes for credit risk modelling is crucial for ensuring accuracy, reliability and compliance. By implementing a consistent framework and approach across all teams, models can be effectively developed, implemented, monitored, validated and updated as needed.

The end-to-end modelling process can be broken down into the following stages:

Import Data - Manipulate Data - Design - Classify - Build - Validate – Deploy.

These stages are the same no matter the modelling algorithm used, from logistic regression to Random Forests.  Using a software tool that can guide modellers through these stages with the necessary checks and controls is invaluable. 

Modeller is an innovative tool that empowers in-house teams with enhanced governance and compliance capabilities, streamlining the end-to-end credit risk modelling process. With its user-friendly interface and advanced features such as model and data version control, audit trails and model documentation, Modeller allows for seamless collaboration between all stakeholders involved in the modelling process and the number and variety of built-in procedures allows creativity from the model builders. This not only saves time but also ensures transparency and accountability, enabling organisations to develop models with confidence and control. With the ever-changing regulatory landscape, having a standardised process using Modeller can give financial institutions a competitive edge while complying with all requirements.

Modeller Governance and Compliance Capabilities

Ensuring compliance and repeatability is crucial when it comes to credit risk modelling. Having a streamlined process in place ensures adherence to modelling requirements and regulations.  This not only helps in meeting regulatory requirements but also ensures consistency and accuracy in the modelling process. By using Modeller, financial institutions can confidently demonstrate their compliance with regulations and have a repeatable process that can be easily replicated for future models. This saves time, reduces costs, and improves overall efficiency of the credit risk modelling process.

Challenges With Tools That Are Less Structured

One of the biggest challenges with tools that are less structured and therefore very flexible, is maintaining consistency and ensuring compliance. The complexity of credit risk modelling makes it susceptible to errors and discrepancies when utilizing less structured tools, leading to significant challenges and potential errors. Tools without the necessary structure and standardisation, often result in variations in methodologies, assumptions, and interpretations between users, leading to discrepancies in results, and outputs which can often be hard to reproduce. 

Furthermore, with multiple stakeholders involved in the credit risk modelling process, effective communication is essential.  Relying on less structured tools can hinder clear communication between team members due to the lack of a standardised framework. This confusion will increase the potential for errors. Moreover, using less structured tools can also limit the ability to track changes made throughout the modelling process accurately. This can compromise transparency and accountability among stakeholders and make it challenging to identify where errors may have occurred.

An example of a less structured tool is Python.  Python is a general purpose programming language which is powerful, versatile, free to use and has become very popular over the last 5 years, particularly for machine learning and data analysis. 

Organisations looking to adopt tools such as Python for their standardised modelling processes are coming across the following challenges:

Integration.  Python has many, many libraries available to perform different activities.  These libraries are not always compatible with each other and therefore cannot be integrated seamlessly for a complete process.

Performance.  Libraries are not always optimised for performance, leading to slow execution times.  Along with Python’s memory management this can cause problems when handling large volumes of data.

Documentation. Libraries are often not documented comprehensively.  As such it’s not always clear exactly how statistics have been calculated, how the output has been produced or how best to use the library. 

New releases and versions. By their nature, open-source libraries may not be maintained regularly which can lead to compatibility and integration issues with newer versions of Python or other libraries.  Code written in one version of Python may not be compatible with another version. An updated library may give a different result to the previous version with no documented reason. Organisations instilling best practice should be controlling the versions of libraries in use by each user.

Code Re-use. Dependent on organisation size, this can become a big problem.  Archiving and re-use of code through tools such as Git and external platforms such as GitHub and Bitbucket are great in theory, but can become a problem in practice. Multiple teams often means that each team creates their own code/program repositories and multiple poorly documented versions often exist all doing very similar (but not identical) activities. 

In contrast, utilising more advanced and structured tools in credit risk modelling minimises these risks by providing a systematic approach that ensures consistency across all stages of modelling. A standardised framework facilitates effective communication among stakeholders and enables traceability of changes made during the modelling process.

In conclusion, while credit risk modelling is an intricate process with many moving parts, utilising structured tools is crucial for its accuracy and effectiveness. It not only minimises errors but also promotes transparency, accountability, and efficient communication among stakeholders. Therefore, financial institutions must invest in advanced technology solutions to enhance their credit risk modelling standards and capabilities for better decision-making outcomes.

With Modeller, the in-house team has access to a highly structured platform that enables them to maintain consistency in their models and ensure compliance with ever-evolving regulations. This not only streamlines the credit risk modelling process but also gives organisations peace of mind knowing that their models are accurate and compliant.

You can find out more about Modeller and see some screenshots here, and to arrange a demo of Modeller please contact us by emailing info@credit-scoring.co.uk.