Business credit rating data—sometimes called corporate or company credit rating data—provides an assessment of a company’s financial solvency and creditworthiness.
Business credit ratings are scored on the company’s payment history, credit history, size, current outstanding debts, and other relevant information. The higher the score, the better the credit rating.
A small number of companies issue credit rankings for most companies in the world: Experian, Equifax, Dun & Bradstreet, and FICO. Equifax issues the Business Credit Report and Experian the Business Credit Score. Dun & Bradstreet’s most commonly used credit ranking is their Credit Score for Businesses, but their PAYDEX Credit Score is also popular. The PAYDEX score is calculated on the company’s payments to suppliers and vendors over the past year.
The FICO LiquidCredit Small Business Scoring Service combines the information issued by the first three companies to generate an average score. The Small Business Administration uses this FICO score to decide whether to approve small business loans.
Of course, there are other scores available, but these are the main ones in use.
Business credit rating models use a mix of structured and unstructured data. The structured data include firmographics, payment history, trade history, and public records like bankruptcies and liens. However, the unstructured data include news reports, company reports, meeting transcripts, and social media posts.
Developers make advances in machine learning constantly, allowing the programs to process this unstructured data more quickly and efficiently. In particular, companies use natural language processing (NLP) to glean insights from these types of online sources. Social media users’ evaluations of companies are highly informative but very difficult for computer programs to evaluate.
Credit rating data determines the financial future of a company, from investments to loans to business credit cards.
AI models enable investors, lenders, and others to evaluate a company’s creditworthiness quickly. Then, provided the data and the model are high quality, investors and lenders can move forward with business.
Companies with less than desirable credit scores may also use the data to make a plan to raise their creditworthiness and become more valuable to investors and suppliers.
Company credit data models that have been thoroughly tested and back-tested against last year’s pre-scored database may run well for years. However, a company’s situation may change, the market or industry may become weaker, etc. Therefore, every so often, the model must be re-calibrated.
Essentially, the most important measure for an effective business credit rating model is how well its output stands up against the S&P Global Ratings Standalone Credit Profile. However, there are many additional tests for data quality which depend on your business goals for this data.
In addition to being complete and frequently updated, every good business credit rating model should have the business strategy defined at the outset. For example, if you are a lender and want to predict whether a company will default on a loan, you will need to carefully note default status for each record and have access to both tradeline and non-tradeline accounts for the company. As another example, if you want to provide a loan to a new company which has little transaction or loan repayment history, the owner’s personal credit history is a good addition to your model.
ZestFinance, in cooperation with Microsoft Cloud, unveils the ZAML Suite to enable lenders to more easily use the more effective machine learning programs to underwrite credit services, “potentially cutting billions in losses for banks and broadening access to credit for tens of millions of people.”
ZestFinance To Deliver First Fully Explainable Artificial Intelligence Solution For Credit Underwriting with Microsoft Cloud
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