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Small Enterprise Default Prediction Modeling through Artificial Neural Networks: An Empirical Analysis of Italian Small Enterprises

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  • Francesco Ciampi
  • Niccolò Gordini

Abstract

Having accurate company default prediction models is vital for both banks and enterprises, especially small enterprises (SEs). These firms represent a vital part in the economy of every country but are also typically more informationally opaque than large corporations. Therefore, these models should be precise but also easily adaptable to suit SE characteristics. Our study applies artificial neural networks (ANNs) to a sample of over 7,000 Italian SEs. Results show that (1) when compared with traditional methods, ANNs can make a better contribution to SE credit‐risk evaluation; and (2) when the model is separately calculated according to size, geographical area, and business sector, ANNs prediction accuracy is markedly higher for the smallest sized firms and for firms operating in entral taly.

Suggested Citation

  • Francesco Ciampi & Niccolò Gordini, 2013. "Small Enterprise Default Prediction Modeling through Artificial Neural Networks: An Empirical Analysis of Italian Small Enterprises," Journal of Small Business Management, Taylor & Francis Journals, vol. 51(1), pages 23-45, January.
    • Handle: RePEc:taf:ujbmxx:v:51:y:2013:i:1:p:23-45
    DOI: 10.1111/j.1540-627X.2012.00376.x
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    Cited by:

    1. Achbah, Rachid & Vitanova, Ivana & Fréchet, Marc, 2024. "Failure Escape: The role of advice seeking in CEOs’ awareness of financial difficulties and corporate restructuring," Journal of Business Research, Elsevier, vol. 175(C).
    2. Carmen Gallucci & Rosalia Santullli & Michele Modina & Vincenzo Formisano, 2023. "Financial ratios, corporate governance and bank-firm information: a Bayesian approach to predict SMEs’ default," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(3), pages 873-892, September.
    3. Peng, Qiaosheng & Zhu, You & Wang, Gang-Jin, 2025. "SCF credit risk assessment with limited labeled data using label propagation algorithm and complex network approaches," International Review of Financial Analysis, Elsevier, vol. 107(C).
    4. Dina Ait Lahcen & Nour-Eddin Amghar, 2025. "Econometric modeling for proactive risk management of financial failure in Moroccan SMEs: a stepwise logistic regression approach in python," Future Business Journal, Springer, vol. 11(1), pages 1-25, December.
    5. Dat Tran & Allan W. Tham, 2025. "Accuracy Comparison Between Feedforward Neural Network, Support Vector Machine and Boosting Ensembles for Financial Risk Evaluation," JRFM, MDPI, vol. 18(4), pages 1-30, April.
    6. Oleh Kolodiziev & Andrii Gukaliuk & Valeriia Shcherbak & Tetiana Riabovolyk & Ilona Androshchuk & Yaryna Pas, 2024. "The Impact of Refugee Startups on Host Country Economies: Business Models and Economic Adaptation," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 2, pages 175-201.
    7. Metzger, Michael & O'Reilly, Seán & Mac an Bhaird, Ciarán, 2025. "Generative artificial intelligence augmenting SME financial management," Technovation, Elsevier, vol. 147(C).
    8. Giulio Gariano & Gianluca Viggiano, 2022. "Press news and social media in credit risk assessment: the experience of Banca d’Italia’s In-house Credit Assessment System," Mercati, infrastrutture, sistemi di pagamento (Markets, Infrastructures, Payment Systems) 24, Bank of Italy, Directorate General for Markets and Payment System.
    9. Flavio Bazzana & Marco Bee & Ahmed Almustfa Hussin Adam Khatir, 2024. "Machine learning techniques for default prediction: an application to small Italian companies," Risk Management, Palgrave Macmillan, vol. 26(1), pages 1-23, February.
    10. Marco Repetto, 2025. "Multicriteria interpretability driven deep learning," Annals of Operations Research, Springer, vol. 346(2), pages 1621-1635, March.
    11. Francesco Columba & Manuel Cugliari & Stefano Di Virgilio, 2026. "Credit Risk Assessment with Stacked Machine Learning," Mercati, infrastrutture, sistemi di pagamento (Markets, Infrastructures, Payment Systems) 73, Bank of Italy, Directorate General for Markets and Payment System.
    12. Weiyu Wang & Maria João Guedes, 2025. "Firm failure prediction for small and medium-sized enterprises and new ventures," Review of Managerial Science, Springer, vol. 19(7), pages 1949-1982, July.
    13. Alessandra Tafuro & Chiara Colamartino & Giuseppe Dammacco & Pierluigi Toma, 2025. "Business Intelligence Systems as innovation tool in the SMEs' Business Control Systems: A Bibliometric Literature Review," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2025(1 Suppl.), pages 121-146.
    14. He-Boong Kwon & Jooh Lee & Laee Choi, 2023. "Dynamic interplay of environmental sustainability and corporate reputation: a combined parametric and nonparametric approach," Annals of Operations Research, Springer, vol. 324(1), pages 687-719, May.

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