The Top 10 Questions to Ask When Choosing Credit Risk Modelling Software
(first article in a series of four)
Introduction
Selecting the right credit risk modelling software is a critical decision for financial institutions seeking to develop and deploy credit risk models efficiently and effectively, whilst complying with guidelines and regulations. To assist you in this crucial process, we've distilled the most important considerations into the top 10 questions you should ask when evaluating credit risk modelling software.
This article introduces the key functionality requirements for model building tools. There are many model building tools available that will satisfactorily address some or all of these requirements. However, answering “Yes” to each of the following questions is only the start. More models, more regulations and more scrutiny mean a lot more work for modelling teams – therefore tools that drive efficiencies, ensure quality outputs and comply with internal standards and external regulations are vital. In the following three articles in this series we will delve deeper into the key areas that provide tremendous benefits to in-house modelling teams; (i) standardisation (ii) domain specific features (iii) human-in-the-loop automation.
Top 10 Questions
Data Reduction
Is it equipped with efficient and powerful data reduction capabilities combining automation with manual intervention, allowing you to focus on the most relevant information for credit risk assessment?
Data Grouping/Binning
Can it perform automated data grouping and binning tasks, whilst also providing insightful visualisation and interactive reviews and updates, streamlining data preparation, data understanding and data engineering for modelling?
Algorithm Comparison
Does it enable you to compare and contrast multiple modelling and machine learning algorithms to identify the best approach for accurate credit risk evaluation?
Open source or tool-maintained code
Does it have its own dedicated routines to perform data and modelling functions that are written, maintained and enhanced by professional developers whilst also offering access to open source packages?
Repeatable Templates
Is there support for creating repeatable templates to standardise the model development process and ensure consistency and reproducibility of results?
Model Control and Engineering
Does it offer intuitive tools for model control and versioning, reject inference and model engineering, empowering you to optimise, refine and re-align models for better performance and/or take account of data anomalies?
Effortless Reporting
Can it generate comprehensive model reports effortlessly and facilitate validation using out-of-time and out-of-sample datasets?
Smooth Implementation
Does it streamline the deployment of models into operational processes, ensuring that modelling results translate into real-world impact seamlessly?
Documentation, Validation, and Audit
Does it simplify model documentation, validation, and auditability, providing a complete audit trail for decision justification and modelling steps taken?
Traceability and Collaboration
Does it allow for effective collaboration within teams, and sharing and review across other teams and users? And can you easily trace and find previous developments, to know exactly how the model was developed and the associated model artefacts?
Conclusion
When choosing credit risk modelling software, these top 10 questions serve as your compass, guiding you towards a best-in-class solution. By considering these vital aspects, you'll be equipped to harness the full potential of your data, develop reliable, robust and powerful models to inform lending decisions, and effectively manage both your credit risk and your model risk. To learn more about how Modeller addresses these questions please get in touch here info@credit-scoring.co.uk
In subsequent articles in this series we will introduce Modeller's strengths across many of the areas listed above, and categorised into three key areas:
Standardisation
Domain specific features
Human-in-the-loop automation
covering features such as data reduction, auto-grouping, reject inference, templates and reporting, model documentation, machine learning algorithms and more.