Using a proprietary database of lending decisions (N=9,898) for small and medium-sized enterprises (SMEs), the paper investigates how banks cope with the adverse selection dilemma. Based on an intertemporal framework, we qualify incorrect and correct lending decisions of banks and investigate the power of lending technologies to predict errors and correct choices. Findings suggest that adverse selection can be better controlled by a durable bank–firm relationship, as well as by an atomistic loan decision process, at the local level. By contrast, a loan decision-making process based exclusively on hard financial information about SMEs may lead to adverse selection errors.
PREDICTIVE STRENGTH OF LENDING TECHNOLOGIES IN FUNDING SMES / Brighi, P.; Lucarelli, C.; Venturelli, V.. - In: JOURNAL OF SMALL BUSINESS MANAGEMENT. - ISSN 0047-2778. - 57:4(2019), pp. 1350-1377. [10.1111/jsbm.12444]
PREDICTIVE STRENGTH OF LENDING TECHNOLOGIES IN FUNDING SMES
Venturelli V.
2019
Abstract
Using a proprietary database of lending decisions (N=9,898) for small and medium-sized enterprises (SMEs), the paper investigates how banks cope with the adverse selection dilemma. Based on an intertemporal framework, we qualify incorrect and correct lending decisions of banks and investigate the power of lending technologies to predict errors and correct choices. Findings suggest that adverse selection can be better controlled by a durable bank–firm relationship, as well as by an atomistic loan decision process, at the local level. By contrast, a loan decision-making process based exclusively on hard financial information about SMEs may lead to adverse selection errors.File | Dimensione | Formato | |
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J Small Business Manag - 2018 - Brighi - Predictive Strength of Lending Technologies in Funding SMEs.pdf
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