Explainable FinTech lending

Golnoosh Babaei, Paolo Giudici and Emanuela Raffinetti

Journal of Economics and Business, 2023, vol. 125-126, No S014861952300019X

Abstract: Lending activities, especially for small and medium enterprises (SMEs), are increasingly based on financial technologies, facilitated by the availability of advanced machine learning (ML) methods that can accurately predict the financial performance of a company from the available data sources. However, despite their high predictive accuracy, ML models may not give users sufficient interpretation of the results. Therefore, it may not be adequate for informed decision-making, as stated, for example, in the recently proposed artificial intelligence (AI) regulations. To fill the gap, we employed Shapley values in the context of model selection. Thus, we propose a model selection method based on predictive accuracy that can be employed for all types of ML models, those with a probabilistic background, as in the current state-of-the-art. We applied our proposal to a credit-scoring database with more than 100,000 SMEs. The empirical findings indicate that the risk of investing in a specific SME can be predicted and interpreted well using a machine-learning model which is both predictively accurate and explainable.

Keywords: Fintech; Credit scoring; Artificial intelligence; Machine learning; Shapley values (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S014861952300019X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jebusi:v:125-126:y:2023:i::s014861952300019x

DOI: 10.1016/j.jeconbus.2023.106126

Access Statistics for this article

Journal of Economics and Business is currently edited by Emanuele Bajo and Moritz Ritter

More articles in Journal of Economics and Business from Elsevier
Bibliographic data for series maintained by Catherine Liu ().