Estimating the credit risk parameter exposure at default is important for banks from an internal risk management and a regulatory perspective. Several approaches are common in the literature and in practice. We theoretically and empirically analyze how the exposure at default should be modeled to obtain accurate estimates of the expected loss. Our empirical analysis is based on a large and unique dataset from a retail portfolio of a European bank. We demonstrate that some approaches can lead to substantially biased estimates of the expected loss and show that the generalized cohort approach is advantageous. Moreover, using in- and out-of-sample analyses, we empirically demonstrate that using the credit conversion factor is preferable to the loan equivalent factor, exposure at default factor, and direct exposure at default estimation to achieve high estimation accuracy.

We investigate whether lenders can realize informational synergies by simultaneously obtaining private information from different accounts of the same borrower. Synergies exist if such information is complementary to each other. We focus on consumer credit, using 3.5 million observations from checking accounts and credit card accounts of the same individuals during 2007–2014. First, activity from both accounts is complementary for estimating consumer default beyond credit scores, borrower characteristics and relationship characteristics. Checking accounts display warning indications about consumer default earlier and more accurately than credit card accounts. Second, decision errors are lower when lenders consider cross-product information. The evidence suggests significant informational synergies that are important for the supply and allocation of credit.