Our approach improves upon traditional credit risk frameworks. These were designed at a time when data was limited and markets moved slowly. That’s why Silicon Valley Bank carried an investment-grade rating as it failed in 2023. The risks were visible, but the models could not keep up. Unlike traditional approaches, the Agio Ratings approach to assessing credit risk reflects the speed of modern finance.

We forecast forward market conditions and reflect their impact on a firm's balance sheet and liquidity. The model can be updated as market conditions change, or new firm data is received.  Our ratings are the product of Bayesian inference where relevant evidence, together with its uncertainty, is distilled into a forward-looking outcome distribution.

Our approach leverages quantitative models that estimate the future financial performance of a company in a range of scenarios. We deliver the precision that modern risk teams need.

Our rating is published with a probability of default (PD) and a loss given default (LGD) forecast which can be combined to calculate expected loss (EL).  The time horizons are specific, and outputs are numerical rather than categorial.