One of the most perplexing elements of D&B’s scoring and analysis continues to be their calculation of conservative and aggressive credit limit recommendations.
The economy is struggling to regain momentum, and small business owners are watching the cost of doing business skyrocket, so why has D&B failed to bring the base guidelines in line to meet the demands of the current millennium?
It seems unfathomable how D&B expects small business owners to maintain a growing business based upon guidelines that have not been adjusted in more than 20 years. We have a client who came to us with credit recommendations that were lower than his company’s annual budget for break-room snacks. While we can help them improve their recommendation levels, D&B truly does need to adjust their algorithms to meet modern income and expense margins.
The discrepancy is mainly due to D&B not adjusting their algorithms to take into account today’s economic environment or factor in inflation over the past two decades. While D&B will try to claim the credit limit recommendations are a reflection of the business industry or the company’s creditworthiness, that kind of reasoning will no longer fly.
The two test cases below are good examples of why the vast majority of vendors and lenders no longer heed D&B’s recommendations when assigning credit capability to their clients.
Client A has a trucking and transportation company with 30 employees and two dozen trucks on the road. He has a strong Paydex© score and all of his risk ratings are in the good to fair range. His annual sales is over $3m and has been profitable for several years. His average checking account balance is mid-six-figures year-round. So why are his D&B credit limit recommendations based at $2,500 conservative to $10,000 aggressive?
D&B’s guidelines are based upon comparing historical credit usage for Client A’s business versus other businesses in the same industry and size. Even though those other businesses didn’t have as strong of a financial forecast or as profitable of a history, the effect of the comparison was skewing the recommendations in the wrong direction. We helped to strengthen his data foundation and build exemplary scores, and that held more value to his banker than the low recommendations, winning him a $300,000 line of credit and interest rates lower than the industry average.