D&B Failure Score
D&B Failure Score is a predictive business credit score developed by Dun & Bradstreet that estimates the likelihood a business will cease operations or fail within the next 12 months. This is evaluated within Business Credit Scores.
Plain-Language Meaning
This score reflects the probability that a company will go out of business or face severe financial distress in the near future, based on statistical analysis of business data.
Practical Example
If you are applying for a business loan, a lender may check your company’s D&B Failure Score to assess the risk of lending to you, since a low score could indicate a higher chance of business closure.
What It Does Not Mean
This score does not measure a company’s profitability, creditworthiness for specific loans, or its ability to pay bills on time; it specifically predicts the risk of business failure.
How the System Uses It
The system evaluates the D&B Failure Score to help lenders, suppliers, and other stakeholders determine the risk of a business failing within a year, influencing decisions about extending credit or entering into contracts.
Common Misconceptions
- “A high D&B Failure Score means a business is more likely to fail.” In fact, a higher score indicates lower risk of failure, while a lower score signals higher risk.
- “The D&B Failure Score is the same as a business credit score.” The Failure Score specifically predicts business closure risk, not overall creditworthiness.
- “Only large companies have a D&B Failure Score.” Dun & Bradstreet calculates this score for businesses of all sizes, provided there is enough data.
Related Pages
Related Glossary Terms
FAQ
- What factors influence the D&B Failure Score? The score is influenced by factors such as payment history, financial ratios, company size, industry risk, public filings, and other business-specific data.
- How often is the D&B Failure Score updated? Dun & Bradstreet updates the Failure Score regularly as new information becomes available, which can be monthly or more frequently depending on data changes.