Experiences of using Python for credit risk modelling
There appears to be two contrasting schools of thought when it comes to credit risk modelling tools. In one camp, practitioners believe Python can do anything and everything, making it the obvious choice. The other camp sees Python’s open-source nature and flexibility as a double-edged sword, creating significant hindrances.
The Python Promise: Flexibility at What Cost?
Python has become the default tool of data science in many organisations for good reason. Its flexibility, extensive libraries, and open-source nature make it a powerful tool. However, when it comes specifically to credit risk modelling, this flexibility introduces challenges. What should be a straightforward task of building a model in Python often turns into a time-consuming technical obstacle course, whereby solving one problem creates another. When building credit risk models with general-purpose programming languages such as Python, modellers often encounter a series of hurdles:
1. Code/package selection dilemma: Which package or library should I use for this specific modelling task? Are there multiple options with overlapping functionality?
2. Environment setup challenges: Ensuring the right versions and dependencies are in place before actual modelling work can begin
3. Compatibility issues: Discovering that chosen packages don't work well together or with your existing infrastructure
4. Documentation gaps: Finding that community-developed tools often lack the comprehensive documentation needed for regulated environments
5. Audit and validation concerns: Determining how to properly validate and document third-party code used to develop regulatory-sensitive models
6. Knowledge transfer burdens: Creating additional documentation so colleagues can understand development and implementation choices, and validate and reproduce results
7. Maintenance responsibility: Dealing with updating and maintaining custom code when packages evolve or certain features are retired
The Specialised Tool Approach: Efficiency with Constraints
In contrast, specialised modelling tools like Paragon’s Modeller offer a standardised and streamlined approach:
1. Select the desired model type
2. Set the parameters
3. Push the Build button
4. Review the fully audited results and iterate as required
The efficiency gain is obvious, but there's a stronger benefit: consistency and reliability across teams. When multiple team members build models using the same standardised tool, they're less likely to implement different solutions to the same problems. This consistency is invaluable in a regulated industry like credit risk.
The Hidden Costs of "Free"
Part of Python's appeal is its zero-cost entry point. However, not everything that is free is truly without cost. The total cost of ownership includes:
• Developer time spent on environment setup and maintenance
• Inconsistency across models and approaches built by different team members
• Regulatory compliance challenges when explaining various implementation approaches
• Knowledge transfer difficulties when team members change
A specialised tool may have upfront costs, but can significantly reduce these hidden expenses over time.
The Mindset of Modellers vs. Programmers
Perhaps the most important distinction isn't in the tools but in the mindset and skill set. Credit risk modelling requires more than programming proficiency - it demands domain expertise, regulatory awareness, and understanding of business context.
An exceptional programmer will not necessarily develop excellent models. An exceptional modeller will not always write elegant and efficient code.
The best credit risk modellers focus on:
• Understanding the business problem
• Interpreting data in its proper context
• Developing models that balance statistical performance with explainability
• Creating solutions that satisfy regulatory requirements and drive business value
These skills often have little to do with pure programming ability.
The Industry Perception Problem
Despite these realities, there's a perception issue. A commonly held view is that you can't be a data scientist or model developer if you are not a Python programmer. This perception creates pressure for professionals to emphasise programming skills on their CVs and resumes, even when their actual modelling expertise might be more valuable. Many experienced modellers who have successfully used specialised tools for years can be reluctant to highlight this experience, instead emphasising their familiarity with Python.
Finding the Right Balance
The ideal approach likely combines elements of both worlds:
• Using specialised tools for standardised, regulated modelling tasks
• Leveraging Python for exploratory analysis, unique problems, and version 0 solutions
• Focusing hiring and training on modelling fundamentals and principals rather than specific programming languages
Questions for Organisations
1. What is the primary goal: code elegance or model effectiveness?
2. How important is consistency across modelling teams, and managing model risk during the development, validation and implementation stages?
3. What are the regulatory and audit documentation requirements?
4. Which approach best supports knowledge transfer, training, repeatability and re-use?
Conclusion
The choice between Python and specialised modelling tools is not merely technical - it reflects fundamental questions about the nature of credit risk modelling itself. Is it primarily a programming exercise or a domain-specific analytical process? The answer lies somewhere in between, but leaning too far towards either extreme can create inefficiencies and risks.
As requirements, methods and regulations continue to evolve, successful organisations will be those that can balance technical flexibility with modelling rigor, recognising that the true value lies not in the tool itself, but in how effectively it enables modellers to apply their expertise to solving real business problems.
Whether you identify as a modeller or a programmer, the ultimate measure of success remains the same: developing effective, compliant, and explainable credit risk models that support sound business decisions.