Mitigating credit risk: modelling and optimizing co-insurance in loan pricing

Abstract

Despite large-scale financial development and banks being the most important credit source globally, banking continues to be plagued by asymmetric information. This uncertainty makes credit risk assessment decisions complex and expensive. In this context, we show how discretionary borrower characteristics, such as the borrower’s network (which can co-insure), help mitigate risk and reduce costs by altering lending decisions. The literature on loan pricing remains focused on objective credit scoring models, while the network literature remains empirical, and borrower based. We fill this void by being the first to theoretically model the lender’s internal decision-making problem when borrowers display discretionary default risk-mitigating attributes such as network strength. We find that the interest rate reduces as the network strength increases. As constraints set in and borrowing becomes more competitive, banks rely even more on network information to parse out better borrowers. Finally, banks substitute monitoring effort with network strength for a more feasible interest rate. This will increase lending, even to borrowers outside the banks’ purview earlier.