Credit Signals

Synnax Credit Signals

Current Credit Signal

The Synnax Current Credit Signal provides an assessment of a company's creditworthiness on a scale from 100 to 0. This score is derived from the average of probabilities predicted by a decentralized and independent network of machine learning models, weighted by each models' accuracy in its previous forecasting of the company's subsequently realized financial data.

Scoring system:

100 - 86 = Superior signal

85 - 71 = Strong signal

70 - 50 = Fair signal

Overall, scores from 100 to 50 represent a credit signal with no significant concerns. However, scores from 49 to 0 indicate varying levels of concern, with lower scores signaling greater risk to the company's credit strength.

The type of concern is identified based on the highest probability of a specific issue, as calculated by the most accurate machine learning models using the latest realized data. If the models predict a probability greater than 50% that a ratee faces a credit concern, the Credit Signal is calculated as a weighted average across three categories:

Profitability Concern

Liquidity Concern

Solvency Concern

The final Credit Signal label is then aligned with the concern category with the highest likelihood of occurrence.

Forecasted Credit Signal

The Synnax Forecasted Credit Signal provides an assessment of a company's future creditworthiness on a scale from 100 to 0. This score is calculated based on the average of future status probabilities predicted by a decentralized and independent network of machine learning models, weighted by each model's accuracy in predicting the company's most recent realized financial data.

Scoring system:

100 - 86 = Superior signal

85 - 71 = Strong signal

70 - 50 = Fair signal

Overall, scores from 100 to 50 indicate no significant concerns regarding the company’s future credit strength. However, scores from 49 to 0 indicate varying levels of concern, with lower scores reflecting greater risk to the company's future credit strength.

The type of concern is identified based on the highest probability of a specific issue, as determined by the most accurate machine learning models using the latest realized data. If the models predict a probability greater than 50% that the company will face a credit concern in the future, the Forecasted Credit Signal is calculated as a weighted average across three categories:

Profitability Concern

Liquidity Concern

Solvency Concern

The final forecasted Credit Signal label is aligned with the category that has the highest likelihood of occurrence.