Tested and proven, our basic predictors, scores and ratings are easy to add to existing workflows to make an immediate impact on the quality of your decisions whether you're in marketing, risk or managing your supply chain. Quick Guide to U.S. Predictive Analytics
The D&B Composite Risk Score also known as the "Triple Play" estimates the overall risk of a business by combining the Viability Rating, Delinquency Predictor, and Total Loss Predictor into a single, comprehensive score. Scale is 3-9, where 3 is the lowest risk and 9 is the highest risk. Available on over 30M businesses in North America.
Use for Granular risk knockout, Segmentation, Prioritization.
The D&B Viability Rating is a multi-dimensional rating that delivers a highly insightful and reliable assessment of a company's future viability based on both predictive and descriptive components. Predictive components predict the likelihood that a company will go out of business, become inactive, or file for bankruptcy over the next 12 months. The descriptive components provide an indication of the amount of predictive data available to make a reliable risk assessment, as well as insight into the age and size of business. The Viability Rating uses the power of the Dun & Bradstreet Data Cloud, which covers over 30M business in North America, taking elements such as business activity signals, detailed commercial payment experiences that capture month-to-month trends, public filing, demographic, and financial information to classify public and private companies into a 1-9 score scale where 1 is the lowest risk and 9 is the highest.
Use to assess business health and viability or find the weak links in your supply chain.
Assess when you'll be paid. See who is late paying bills. Predicts the likelihood that a company will pay in a severely delinquent manner (10% or more of their obligations 91+ days past term), seek legal relief from creditors, or cease operations without paying all creditors in full during the next 12 months. Offers superior performance over past credit scores. Multiple client validations suggest at least 23% lift over CCS 8. Key ingredients in the model include business activity signals, detailed commercial payment experiences that capture month-to-month trends, public filing, demographic, and financial information. Offered on 3 different scales: Risk Class 1-5, Percentile Ranking 1-100, and Raw Score 101-660 with delinquency rates ranging from high of 52% to low of 1.1%. Available on over 30M North America businesses.
Use for Risk knockout, segmentation and/or prioritization.
Predicts the probability of a company never paying, based on their similarities with other companies in the Dun & Bradstreet Data Cloud that don't pay. Never paying is defined at the account level whereby the total balance owing since the time of origination rolls to 121+ days past due. A "bad" business is one with 80% or more of its dollars owing associated with these "never pay" account originations. This predictor is designed to identify first time payment default, straight rollers, or fictitious/shell companies. It is not designed to identity never pay performance due to theft, account takeovers, or bust outs. The Total Loss Predictor score uses statistical probabilities to classify companies into two risk classifications: a 2002-2999 Risk Score and a 1-10 Risk Class. These classifications are based on the probability of a business experiencing the above definition of 'never pay' over the next 9-month period.
Use to increase your confidence in saying "Yes" more often. Ideal for risk knockout, segmentation and prioritization.
Dun & Bradstreet collects, curates, and archives data on commercial entities - large and small - across a wide spectrum of channels. Most of the data is in a "raw" (very basic and elemental) form. To increase the efficiency and effectiveness in using this data in predictive models, we apply experience-driven transformations and rollups to concentrate and condense the information contained in the more fundamental raw data elements into Derived attributes for use as predictor variables in risk and marketing models.
Derived attributes are available for transactional use in Toolkit™, and Dunslink™. Data sources used to create Derived Attributes for Transactional use include Detailed Trade, Business Activity, D&B Inquiry and Business Spending. Further, Dun & Bradstreet has a high level of confidence in the continued production and availability of all of the derived attributes for the foreseeable future.
An innovative solution designed to help you get an earlier picture of business risk or opportunity in ways that traditional assessments cannot provide. Advanced analytics are cutting through all the data that flows through the Dun & Bradstreet Data Cloud to transform 'digital smoke signals' into early warnings of possible future business behavior.
For example, a combination of material change events like an increase in spend, new site openings, and addition of new credit lines may be predictive of a business that is poised for growth and about to increase its buying power. This foresight will help clients anticipate behavior and place them ahead of the competition.
The scale for Material Change Segmentation is A-J indicating which of the following segments a company is in: early sign of decay, decreasing demand, increase in borrowing, increase in scale, increase in demand, leverage for growth, organic growth, reduction in scale, spend growth, stable.
D&B's Small Business Health Index (SBHI) measures business health at the Metropolitan Statistical Area (MSA) and Industry (SIC) level as it relates to payment patterns, failure rates, and utilization on credit. The index is a combination of pro-cyclical and counter-cyclical elements - reflected in one number and is calculated quarterly. The Index is based on 4 factors:
- Average Credit Card Utilization
- Percent of Credit Cards with outstanding balance cycle3+ (61DPD+)
- Ratio derived from the number of failures in the last 12 months over prior 12 months
- Percent of delinquent dollars 91DPD+ out of all outstanding balance.