Banks and Borrower Networks : To Lend or Not to Lend?

Banks remain the most important credit source globally despite large-scale financial development. However, banking continues to be plagued by rising costs and information asymmetry. In this context, we show how an additional borrower characteristic, specifically the borrower's network strength, helps reduce costs by altering the bank's lending decisions. The literature on borrower network remains largely empirical and borrower-based. We fill this void by being the first to theoretically model the lender's decision making problem with network effect. We specifically examine how a bank sets the interest rate when networks work as a default risk-mitigating attribute. We find that the interest rate reduces as 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, bank's substitute monitoring effort with network strength for a more feasible interest rate. This will increase lending, even to borrower's outside the banks' purview earlier. Thus, the proposed model can help banks alter their subjective lending decisions to maximize lending and profits. More so, in a data driven world. This has large-scale economic benefits for all stakeholders - banks and other financial institutions, borrowing firms and the economy at large