Credit Analytics

Credit analytics refers to a set of quantitative models and analytical techniques used to assess, measure, and monitor the credit risk of counterparties such as corporates and financial institutions. It enables lenders, investors, and risk managers to evaluate a borrower’s creditworthiness, anticipate potential defaults, estimate losses, and make informed credit decisions. Typical use cases include credit approval, portfolio risk monitoring, early warning detection, regulatory capital and expected credit loss calculations, and exposure management across public and private companies on a global scale.

Core Credit Analytics Models:

• Credit Model (CM)
  The Credit Model is a statistical credit scoring model that uses company financials and relevant macroeconomic factors to generate a quantitative credit score that aligns with long‑term credit ratings. It is trained on S&P Global Ratings data and is designed to assess the credit risk of public and private companies across developed, emerging, and frontier markets, producing stable through‑the‑cycle credit risk scores.
• PD Model Fundamentals (PDFN)
  PD Model Fundamentals is a reduced‑form Probability of Default model trained on default indicators. It incorporates both financial risk and business risk to estimate PD values over horizons ranging from one year to more than 35 years. The model applies to public and private companies of all sizes and maps PDs to credit scores, with stability aligned to changes in company fundamentals.
• PD Model Market Signals (PDMS)
  PD Model Market Signals is a market‑driven credit risk model based on an enhanced Merton structural framework. It derives short‑term, point‑in‑time PDs from market‑implied signals and provides timely early‑warning indicators for credit deterioration. The model is updated daily and is applicable to public companies, linking market movements directly to changes in credit risk.