The Impact of Inter-Firm Financial Transactions on the Credit Risk of SMEs. / Vinciotti, Veronica; Tosetti, Elisa; Moscone, Francesco; Lycett, Mark.

In: Journal of the Royal Statistical Society: Series A (Statistics in Society), 09.07.2019.

Research output: Contribution to journalArticle

Forthcoming

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The Impact of Inter-Firm Financial Transactions on the Credit Risk of SMEs. / Vinciotti, Veronica; Tosetti, Elisa; Moscone, Francesco; Lycett, Mark.

In: Journal of the Royal Statistical Society: Series A (Statistics in Society), 09.07.2019.

Research output: Contribution to journalArticle

Harvard

Vinciotti, V, Tosetti, E, Moscone, F & Lycett, M 2019, 'The Impact of Inter-Firm Financial Transactions on the Credit Risk of SMEs', Journal of the Royal Statistical Society: Series A (Statistics in Society).

APA

Vinciotti, V., Tosetti, E., Moscone, F., & Lycett, M. (Accepted/In press). The Impact of Inter-Firm Financial Transactions on the Credit Risk of SMEs. Journal of the Royal Statistical Society: Series A (Statistics in Society).

Vancouver

Vinciotti V, Tosetti E, Moscone F, Lycett M. The Impact of Inter-Firm Financial Transactions on the Credit Risk of SMEs. Journal of the Royal Statistical Society: Series A (Statistics in Society). 2019 Jul 9.

Author

Vinciotti, Veronica ; Tosetti, Elisa ; Moscone, Francesco ; Lycett, Mark. / The Impact of Inter-Firm Financial Transactions on the Credit Risk of SMEs. In: Journal of the Royal Statistical Society: Series A (Statistics in Society). 2019.

BibTeX

@article{4c7641e8854b4ab8abdaca4e7f08a72b,
title = "The Impact of Inter-Firm Financial Transactions on the Credit Risk of SMEs",
abstract = "Despite the recognised importance of inter-firm financial links in determininga company’s performance, only few studies have incorporated proxies for inter-firm links into credit risk models, and none of these use real financial transactions. In this paper, we estimate a credit risk model for small and medium-sized enterprises, augmented with information on observed inter-firm financial transactions. We exploit a novel data set on about 60000 companies based in the UK and their financial transactions over the years 2015 and 2016. We develop a number of network-augmented credit risk models and compare their prediction performance with that of a conventional credit risk model that only includes a set of financial ratios. We find that augmenting a default risk model with information on the transaction network makes a significant contribution to increasing the default prediction power of risk models built specifically for small and medium-sized enterprises. Our results may help bankers and credit scoring agencies to improve the credit scoring of thesecompanies, ultimately reducing their propensity to apply excessive lending restrictions.",
keywords = "SME, Credit risk modelling, Networks, Financial transactions",
author = "Veronica Vinciotti and Elisa Tosetti and Francesco Moscone and Mark Lycett",
year = "2019",
month = "7",
day = "9",
language = "English",
journal = "Journal of the Royal Statistical Society: Series A (Statistics in Society)",
issn = "0964-1998",
publisher = "Wiley-Blackwell",

}

RIS

TY - JOUR

T1 - The Impact of Inter-Firm Financial Transactions on the Credit Risk of SMEs

AU - Vinciotti, Veronica

AU - Tosetti, Elisa

AU - Moscone, Francesco

AU - Lycett, Mark

PY - 2019/7/9

Y1 - 2019/7/9

N2 - Despite the recognised importance of inter-firm financial links in determininga company’s performance, only few studies have incorporated proxies for inter-firm links into credit risk models, and none of these use real financial transactions. In this paper, we estimate a credit risk model for small and medium-sized enterprises, augmented with information on observed inter-firm financial transactions. We exploit a novel data set on about 60000 companies based in the UK and their financial transactions over the years 2015 and 2016. We develop a number of network-augmented credit risk models and compare their prediction performance with that of a conventional credit risk model that only includes a set of financial ratios. We find that augmenting a default risk model with information on the transaction network makes a significant contribution to increasing the default prediction power of risk models built specifically for small and medium-sized enterprises. Our results may help bankers and credit scoring agencies to improve the credit scoring of thesecompanies, ultimately reducing their propensity to apply excessive lending restrictions.

AB - Despite the recognised importance of inter-firm financial links in determininga company’s performance, only few studies have incorporated proxies for inter-firm links into credit risk models, and none of these use real financial transactions. In this paper, we estimate a credit risk model for small and medium-sized enterprises, augmented with information on observed inter-firm financial transactions. We exploit a novel data set on about 60000 companies based in the UK and their financial transactions over the years 2015 and 2016. We develop a number of network-augmented credit risk models and compare their prediction performance with that of a conventional credit risk model that only includes a set of financial ratios. We find that augmenting a default risk model with information on the transaction network makes a significant contribution to increasing the default prediction power of risk models built specifically for small and medium-sized enterprises. Our results may help bankers and credit scoring agencies to improve the credit scoring of thesecompanies, ultimately reducing their propensity to apply excessive lending restrictions.

KW - SME

KW - Credit risk modelling

KW - Networks

KW - Financial transactions

M3 - Article

JO - Journal of the Royal Statistical Society: Series A (Statistics in Society)

JF - Journal of the Royal Statistical Society: Series A (Statistics in Society)

SN - 0964-1998

ER -