Project Summary
Company
Private Credit Asset Manager
Industry
Financial Services
Services
Credit Risk Modeling and Risk Analysis
Location
London, United Kingdom
A leading private credit asset manager needed to enhance its credit risk model to meet the demands of complex, modern risk assessments.
Their existing model lacked the precision to fully capture the relationships between various risk factors, making it difficult to manage unexpected changes in credit exposure accurately.
The firm aimed to build a custom fully coded solution, avoiding the limitations of an Excel-based setup. They required a model that was not only more accurate but also faster and easier for their analysts to use.
Our Solution Delivered:
Advanced Correlation Calculations: Enabled realistic modeling of risk factor interdependencies for more accurate scenario analysis.
Predictive Overcall Mechanism: Enabled better forecasts of credit exposure under different scenarios.
Seamless System Integration with User-Friendly Interface: Integrated directly into the client’s existing system and data pipeline with an easy-to-use interface.
The Challenge
A leading private credit asset manager identified that its existing credit risk model needed updates to stay effective with modern risk assessment standards. The company wanted to achieve a level of precision similar to latest industry standards, but in a fully custom and controlled setup. The model needed to capture interdependencies between various risk factors and manage unpredictable changes in credit exposure with accuracy. Additionally, they required a seamless integration with their existing system, avoiding the limitations of Excel-based models.
The objectives were twofold:
Enhance predictive accuracy and scenario reliability.
Ensure these improvements fit smoothly into their current modeling framework. With these advancements, the firm aimed to elevate its credit risk capabilities for quicker, more reliable decision-making.
The Approach
To meet these goals, we implemented focused enhancements to the credit risk model. The first enhancement involved incorporating correlation calculations based on a multi-factor Gaussian copula model, allowing the model to better represent relationships between risk factors like market rates and economic trends. This added depth gave the model more realistic, scenario-specific insights.
Next, we introduced a predictive overcall mechanism to anticipate changes in credit exposure under various scenarios. This mechanism strengthened the model’s resilience and accuracy, even in volatile conditions. We also developed a custom interface for rule-based qualitative assessments, enabling a comprehensive review of qualitative risk factors. This integration provided the company’s analysts with a well-rounded tool that combined quantitative and qualitative analysis in one place.
Our team worked closely with the client in regular feedback sessions, ensuring that each feature met operational and regulatory standards. This collaborative approach kept the project aligned with the client’s objectives.
The Impact
The upgraded model delivered significant improvements in both predictive precision and operational efficiency. Correlation-driven simulations offered a deeper insight into dependencies among risk factors, resulting in more reliable scenario modeling. The predictive overcall mechanism enhanced the model’s ability to anticipate credit exposures, building greater confidence in the firm’s risk assessment capabilities.
The optimized setup, integrated seamlessly into the client’s system, provided an efficient, user-friendly interface that enabled analysts to make faster, data-informed credit assessments. The added qualitative assessment feature allowed a balanced view of risk, combining quantitative and qualitative insights for a more comprehensive approach to credit evaluation.
