Assessing corporate financial risk is a complex process that requires a structured, data-driven approach. Traditional rating systems rely heavily on expert judgment, which, while valuable, can be subjective and time-consuming. A newly formed rating agency needed to develop a reliable, scalable, and standardized system for evaluating corporate risk. The goal was to build robust statistical and semi-expert models that could accurately assess the creditworthiness of companies, integrating diverse financial data sources.
One of the key challenges was defining the concept of default in a way that was both theoretically sound and practically applicable. Additionally, the project required sourcing and integrating external data, including financial reports and credit bureau records. The agency also needed a calibration process that would align risk scores with established industry benchmarks, ensuring that ratings were interpretable and consistent with existing market standards. Finally, the models had to be documented and structured in a way that allowed for easy updates, external validation, and integration into the agency’s IT infrastructure.

To address these challenges, we developed a corporate rating system that combines statistical modeling with semi-expert evaluation. The first step was defining a precise methodology for identifying default events, ensuring that the model’s risk assessments were based on consistent and objective criteria.
We then established a process for collecting and integrating financial data with external data sources. This required designing a custom data preparation framework that structured financial statements and merged them with credit bureau data. The resulting dataset allowed for a comprehensive analysis of corporate risk factors, capturing both financial and behavioral indicators.
The core of the system was a set of statistical models designed to analyze the likelihood of default based on historical data. These models were built using carefully chosen mathematical techniques to ensure accuracy and robustness. To complement the statistical approach, we developed a semi-expert framework that allowed human analysts to refine and adjust model outputs based on industry-specific knowledge.
A key component of the project was calibrating the models to align with a standardized risk scale. This calibration ensured that the rating system produced outputs that were interpretable and comparable to established rating methodologies. The entire process was iterative, with ongoing discussions with client experts to refine the models based on real-world considerations.
To ensure long-term usability, we provided comprehensive model documentation, including reproducible code that allows for future updates and refinements. We also supported the client’s IT partner in integrating the rating system into the agency’s infrastructure, ensuring seamless deployment and usability.

The rating system successfully enabled the agency to assess corporate risk using a combination of statistical modeling and expert-driven evaluation. The solution provided a standardized method for evaluating creditworthiness, reducing reliance on manual assessments while maintaining expert oversight.
By integrating financial data with external business intelligence, the system enhanced the accuracy of risk predictions. The custom calibration process ensured that ratings aligned with industry benchmarks, increasing their credibility in the financial sector. The models were thoroughly documented and validated, allowing for continuous updates as market conditions evolve.
With a structured, data-driven approach to corporate risk assessment, the rating agency now has a scalable and reliable system for evaluating company creditworthiness. The integration of statistical and expert-based methodologies ensures that risk assessments are both objective and adaptable, positioning the agency as a credible player in the market.
By combining mathematical modeling with expert insights, this corporate rating system delivers a powerful tool for assessing financial risk. It equips the rating agency with a scalable, objective, and data-driven approach to evaluating corporate creditworthiness, enhancing decision-making and market credibility.

The project involved defining key rating criteria, including the definition of default events and the selection of external data sources. A structured data preparation methodology was developed to deliver quality data to build statistical models. The model-building process was iterative, incorporating feedback from industry experts to refine the statistical and semi-expert frameworks.
Custom calibration techniques were implemented to align model outputs with industry-standard rating scales. The final phase included preparing detailed model documentation and supporting IT partners in developing and integrating the system into the agency’s operational infrastructure.

The system was built using statistical modeling techniques and semi-expert methodologies, ensuring both data-driven accuracy and expert validation. The financial data was preprocessed using a custom-designed framework to integrate diverse information sources effectively. The models underwent iterative refinement and calibration to align with industry-standard rating scales, providing reliable and interpretable outputs.