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Cited by 22Financial Distress and Bankruptcy PredictionWorking Capital and Financial PerformanceBanking stability, regulation, efficiency

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Predicting corporate credit risk: Network contagion via trade credit

Open access
Berloco, Claudia, De Francisci Morales, Gianmarco, Frassineti, Daniele, Greco, Greta, Kumarasinghe, Hashani, Lamieri, Marco, Massaro, Emanuele, Miola, Arianna, Yang, Shuyi·2021·PLOS ONE
DOI 10.1371/journal.pone.0250115PMC PMC8084139
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Abstract

Trade credit is a payment extension granted by a selling firm to its customer. Companies typically respond to late payments from their customers by delaying payments to suppliers, thus generating a ripple through the transaction network. Therefore, trade credit is as a potential vehicle of propagation of losses in case of default events. The goal of this work is to leverage information on the trade credit among connected firms to predict imminent defaults of firms. We use a unique dataset of client firms of a major Italian bank to investigate firm bankruptcy between October 2016 to March 2018. We develop a model to capture network spillover effects originating from the supply chain on the probability of default of each firm via a sequential approach: the output of a first model component on single firm features is used in a subsequent model which captures network spillovers. While the first component is the standard econometrics way to predict such dynamics, the network module represents an innovative way to look into the effect of trade credit on default probability. This module looks at the transaction network of the firm, as inferred from the payments transiting via the bank, in order to identify the trade partners of the firm. By using several features extracted from the network of transactions, this model is able to predict a large fraction of the defaults, thus showing the value hidden in the network information. Finally, we merge firm and network features with a machine learning model to create a ‘hybrid’ model, which improves the recall for the task by almost 20 percentage points over the baseline.

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Figures

Fig 1 Size of the target perimeter PT and the extended perimeter PE across time.
Fig 1 Size of the target perimeter PT and the extended perimeter PE across time.CC BY · PubMed Central
Fig 2 (a) A component of the inter-firm business transaction networks. The nodes are firms and links are business transactions from customers to providers, indicating a money flow and thus a possible trade debt. In a similar way, we assume that also the risk flow, i.e., propagates, between adjacent nodes. The nodes are firms and links are risk propagations from an insolvent firm to its adjacent nodes. (b) degree distribution of the transactions networks (blue points correspond to incoming degree (kin) while orange to outgoing degree (kout)). The degree of the networks shows a typical scale free distribution, i.e. y ∼ kγ: here γ = −2.54 and γ = −2.50 for the incoming and outgoing nodes’ degree distribution.
Fig 2 (a) A component of the inter-firm business transaction networks. The nodes are firms and links are business transactions from customers to providers, indicating a money flow and thus a possible trade debt. In a similar way, we assume that also the risk flow, i.e., propagates, between adjacent nodes. The nodes are firms and links are risk propagations from an insolvent firm to its adjacent nodes. (b) degree distribution of the transactions networks (blue points correspond to incoming degree (kin) while orange to outgoing degree (kout)). The degree of the networks shows a typical scale free distribution, i.e. y ∼ kγ: here γ = −2.54 and γ = −2.50 for the incoming and outgoing nodes’ degree distribution.CC BY · PubMed Central
Fig 3 Distribution of (a) the number and (b) the amount of transactions. (c-d) Scaling relations between amount of transactions and degrees in log-log scale.
Fig 3 Distribution of (a) the number and (b) the amount of transactions. (c-d) Scaling relations between amount of transactions and degrees in log-log scale.CC BY · PubMed Central
Fig 4 Distribution of the relative knowledge of the volume of transactions in comparison with the total revenue for the target perimeter.
Fig 4 Distribution of the relative knowledge of the volume of transactions in comparison with the total revenue for the target perimeter.CC BY · PubMed Central
Fig 5 Percentage of adjacent nodes of insolvent firms at time t that experienced a default δ months later (for δ ∈ {3, 6, 9, 12}).
Fig 5 Percentage of adjacent nodes of insolvent firms at time t that experienced a default δ months later (for δ ∈ {3, 6, 9, 12}).CC BY · PubMed Central
Fig 6 Recall@K and Precision@K for the random forest model based on firm features as a function of time. The average values for the score are R@K = 63.9%, P@K = 8.5%, F1 = 14.8%.
Fig 6 Recall@K and Precision@K for the random forest model based on firm features as a function of time. The average values for the score are R@K = 63.9%, P@K = 8.5%, F1 = 14.8%.CC BY · PubMed Central
Fig 7 Relative feature importance for the firm features in the random forest model.
Fig 7 Relative feature importance for the firm features in the random forest model.CC BY · PubMed Central
Fig 8 Recall@5% for the network model using a single step model with the PPR as unique feature for different values of α.
Fig 8 Recall@5% for the network model using a single step model with the PPR as unique feature for different values of α.CC BY · PubMed Central
Fig 9 Recall@5% for the network model using different temporal discounting for the restart vector Q. The dashed lines correspond to the linear discount while the dashed lines with triangular markers to the exponential temporal discounting.
Fig 9 Recall@5% for the network model using different temporal discounting for the restart vector Q. The dashed lines correspond to the linear discount while the dashed lines with triangular markers to the exponential temporal discounting.CC BY · PubMed Central
Fig 10 Recall@K and Precision@K of the 2-step model on the target perimeter.
Fig 10 Recall@K and Precision@K of the 2-step model on the target perimeter.CC BY · PubMed Central
Fig 11 Recall@K for the XGBoost model with mixed single-firm and network features as a function of time in the prequential setting compared to a logistic regression model on single-firm features (baseline). The average R@K is 68.1% and the AUC is 90.5%.
Fig 11 Recall@K for the XGBoost model with mixed single-firm and network features as a function of time in the prequential setting compared to a logistic regression model on single-firm features (baseline). The average R@K is 68.1% and the AUC is 90.5%.CC BY · PubMed Central

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Citations

Cites

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Metadata

Authors
Berloco, C., De Francisci Morales, G., Frassineti, D., Greco, G., Kumarasinghe, H., Lamieri, M., Massaro, E., Miola, A., & Yang, S.
Affiliations
  • Intesa Sanpaolo (Italy) (IT)
  • Novartis (Spain) (ES)
  • Institute for Scientific Interchange (IT)
Corresponding author
  • Berloco, Claudia
  • Lamieri, Marco
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2021-04-29
Volume
16
Issue
4
Pages
e0250115
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0250115
PMC
PMC8084139
OpenAlex
W3159623064
Cited by
22
References
1
Citations by year
2026: 2 · 2025: 4 · 2024: 5 · 2023: 4 · 2022: 6 · 2021: 1
FWCI
6.71
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • BOA (University of Milano-Bicocca) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Institutional Research Information System University of Turin (University of Turin) (submittedVersion)
Funders
Intesa Sanpaolo Innovation Center
Topics
  • Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
  • Social Sciences › Business, Management and Accounting › Accounting › Working Capital and Financial Performance
  • Social Sciences › Economics, Econometrics and Finance › Finance › Banking stability, regulation, efficiency
Keywords
trade credit, corporate credit risk, default prediction, network contagion, supply chain, transaction networks, bankruptcy prediction, hybrid machine learning, Italian firms
MeSH terms
Machine Learning, Commerce, Financial Management, Forecasting, Humans, Probability, Professional Corporations, Bankruptcy, Models, Economic

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