Article: Monday, 24 March 2014
Bad loans are made in boom times. Good loans are made in recessionary times. Lenders such as suppliers who provide trade credit or banks would be well advised to remember this simple dictum whenever they are approached for credit by a borrower not entirely familiar to them.
It is crucial for lenders to assess the default risk of their borrowers accurately, especially in good times when low-quality borrowers might look like high-quality borrowers. A credit information sharing system helps lenders to do a better job in assessing borrower credit quality.
This is one of the key issues to emerge in discussions arising from a recent paper I co-wrote on the subject of credit information pooling, Business credit information sharing and default risk of private firms, which was published in the Journal of Banking and Finance in August 2013
It is an intriguing and often overlooked fact about credit markets. During the boom times, credit providers tend to become less strict, they weaken their screening and monitoring, and their credit decisions become more relaxed. Then when recession kicks in, borrowers that looked good on paper unexpectedly go into default. The system we outline in our paper adds value by separating future good loans from future bad loans. This is particularly useful for small businesses during the boom period.
We are talking about pre-emptive action to pool credit information on unlisted borrowers with the aim of avoiding future loan losses. The notion is given further depth by the experiences of recent years, when smaller companies have found credit more difficult to obtain, despite the near-zero-cost liquidity poured into many markets by central banks, including the US Federal Reserve, the European Central Bank and the Bank of England. Even Switzerland's central bank and the Bank of Japan finally found themselves more or less forced into participating in what is often referred to as the greatest monetary experiment in history.
Credit information sharing works. The world's major credit agencies have demonstrated this for decades, gathering information about listed companies and sharing their opinion of creditworthiness. However, we never fully understood why. We show that it works because the accuracy of default prediction is significantly improved. This results in a more efficient allocation of credit. If our recommendations are followed through, credit will flow to the better companies that can use it effectively in growing their business, and service it effortlessly. It will, meanwhile, be denied to weaker companies who would use it to buy time by supporting unprofitable activities.
I envisage the credit pooling of the future as complementary to existing information gathering and dissemination activity rather than a replacement for it. Its prime value will lie in the initiation of new lender-borrower relationships rather than in bolstering existing ones. At an intellectual level, this will improve the quality of credit-making decisions. At a practical level, it will boost the profitability on both sides of the equation. Lenders will experience fewer defaults. Borrowers will enjoy lower-cost financing as the strong will no longer subsidise their weaker counterparts as lenders cease granting credit to firms that cannot repay it.
In our study we provide a direct examination of whether and how business credit information sharing helps to better assess the default risk of private firms. The analysis is based on a representative panel dataset from the largest commercial credit bureau in Germany and includes firms from all major industries.
First, we find that business credit information sharing substantially improves the accuracy of aggregate and firm-specific default predictions. We interpret our result as novel, and direct evidence for the channel that explains why credit information sharing exerts a positive influence on credit availability, cost of credit and realised credit risk. In other words, through this channel (i.e., the improvement in default prediction accuracy associated with business credit information sharing) it is possible to achieve a better credit allocation in the economy.
While the effect is found in most industries, we also measure a substantial heterogeneity in the value of business credit information across industries. This finding is also new since previous studies are either conducted at the country level or based on firms from single industries.
Second, we provide evidence on the factors that influence the magnitude of the value of business credit information sharing for private firms. The default prediction accuracy is improved for older firms and those with limited liability, and it depends on the sharing of firms’ payment history and the number of firms covered by a local credit bureau office. The value of soft business credit information sharing is higher for smaller and less distant firms.
Third, we show that the higher the value of credit business information the lower the realised default rates. This result is confirmed in spatial and industry analyses and provides direct evidence that the improvement in default prediction accuracy due to credit information sharing serves as a channel that leads to a more efficient credit allocation.
We extend and complement the existing literature by providing new evidence on the channel through which business credit information sharing adds value and on the factors that influence its strength. Because private firms, especially SMEs, are of key importance for economic activity, employment and innovation in many countries, we believe that our study may have broader implications about the impact of business credit information sharing.
Rotterdam School of Management, Erasmus University
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