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A network based fintech inclusion platform

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Listed:
  • Ahelegbey, Daniel
  • Giudici, Paolo
  • Pediroda, Valentino

Abstract

The paper evaluates, from a sustainable finance viewpoint, a machine learning model implemented in a fintech platform, whose aim is to assign credit ratings. The aim of the model is to learn from both micro economic data and macro economic trends the credit rating of companies that ask for credit. We show that the proposed model is able to reward the companies that have better financial performances with better ratings and, therefore, a higher probability/lower cost of obtaining credit. At the same time, the model correctly takes into account the overall evolution of the economy, favoring financial inclusion for the more penalized economic sectors, particularly during crisis times. The model, its application to credit rating, and its evaluation, are illustrated with reference to more than 100,000 European companies before and during the COVID-19 pandemic crisis. The results shows that, while the impact of the financial variables does not change over time, and particularly during the pandemic, the impact of sectors changes considerably, favoring financial inclusion and resilience.

Suggested Citation

  • Ahelegbey, Daniel & Giudici, Paolo & Pediroda, Valentino, 2023. "A network based fintech inclusion platform," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    • Handle: RePEc:eee:soceps:v:87:y:2023:i:pb:s0038012123000551
    DOI: 10.1016/j.seps.2023.101555
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    References listed on IDEAS

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    1. Ahelegbey, Daniel Felix & Giudici, Paolo & Hadji-Misheva, Branka, 2019. "Latent factor models for credit scoring in P2P systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 112-121.
    2. Paolo Giudici, 2001. "Bayesian data mining, with application to benchmarking and credit scoring," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 17(1), pages 69-81, January.
    3. Robert C. Merton, 2005. "Theory of rational option pricing," World Scientific Book Chapters, in: Sudipto Bhattacharya & George M Constantinides (ed.), Theory Of Valuation, chapter 8, pages 229-288, World Scientific Publishing Co. Pte. Ltd..
    4. Luis Javier Sánchez Barrios & Galina Andreeva & Jake Ansell, 2014. "Monetary and relative scorecards to assess profits in consumer revolving credit," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 443-453, March.
    5. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    6. Niklas Bussmann & Paolo Giudici & Dimitri Marinelli & Jochen Papenbrock, 2021. "Explainable Machine Learning in Credit Risk Management," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 203-216, January.
    7. Craig Friedman & Sven Sandow, 2003. "Model Performance Measures For Expected Utility Maximizing Investors," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 6(04), pages 355-401.
    8. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    9. Christian Haddad & Lars Hornuf, 2019. "The emergence of the global fintech market: economic and technological determinants," Small Business Economics, Springer, vol. 53(1), pages 81-105, June.
    10. Andreeva, Galina & Ansell, Jake & Crook, Jonathan, 2007. "Modelling profitability using survival combination scores," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1537-1549, December.
    11. Riza Emekter & Yanbin Tu & Benjamas Jirasakuldech & Min Lu, 2015. "Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending," Applied Economics, Taylor & Francis Journals, vol. 47(1), pages 54-70, January.
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