Abstract
Credit assessments activities are essential for financial institutions and allow the global economy to grow. Building robust, solid and accurate models that estimate the probability of a default of a company is mandatory for credit insurance companies, specially when it comes to bridging the trade finance gap. The recent developments in Artificial Intelligence are offering new powerful opportunities. However, most AI techniques are labeled as black-box models due to their lack of explainability. For both users and regulators, in order to deploy such technologies at scale, being able to understand the model logic is a must to grant accurate and ethical decision making. In this study, we focus on companies credit scoring and we benchmark different machine learning models. The aim is to build a model to predict whether a company will experience financial problems in a given time horizon. We address the black-box problem using eXplainable Artificial Techniques—in particular, post hoc explanations using SHapley Additive exPlanations. We bring light by providing an expert-aligned feature relevance score highlighting the disagreement between a credit risk expert and a model feature attribution explanation in order to better quantify the convergence toward a better human-aligned decision making.
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27 July 2022
A Correction to this paper has been published: https://doi.org/10.1007/s11760-022-02304-1
Notes
After trying different combinations for both hyperparameters, these values obtain the best results for the tested ML models.
References
Hand, D.J., Henley, W.E.: Statistical classification methods in consumer credit scoring: a review. J. R. Stat. Soc. Ser. A Stat. Soc. 160(3), 523–541 (1997)
Altman, E.I.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J. Financ. 23(4), 589–609 (1968)
Merton, R.C.: On the Pricing of Corporate Debt: the Risk Structure of Interest Rates. J. Financ. 29(2), 449–470 (1974)
Rikkers, F., Thibeault, A: The influence of rating philosophy on regulatory capital and procyclicality. In: European Financial Management Association, Annual Meeting, June 24–28, Athens, Greece. Nyenrode Business Universiteit. 2008
Lessmann, S., et al.: Benchmarking state-of- the-art classification algorithms for credit scoring: an update of research. Eur. J. Oper. Res. 247(1), 124–136 (2015)
Lundberg, S.M., Lee, S.-.I.: A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30 (2017).
Makowski, P.: Credit scoring branches out. Credit World 75(1), 30–37 (1985)
Baesens, B., et al.: Benchmarking state-of-the-art classification algorithms for credit scoring. J. Oper. Res. Soc. 54(6), 627–635 (2003)
Breiman, L.: Random forests. English. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324
Bussmann, N., et al.: Explainable machine learning in credit risk management. Computat. Econ. (2020)
Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016)
Chawla, N.V., et al.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002). https://doi.org/10.1613/jair.953
Kuiper, O., et al.: Exploring explainable AI in the financial sector: perspectives of banks and supervisory authorities. In: Benelux Conference on Artificial Intelligence. Springer, pp. 105– 119 (2021)
Guidotti, R., et al.: A survey of methods for explaining black box models. ACM Comput. Surv. (CSUR) 51(5), 1–42 (2018)
Lundberg, S., Lee, S.- I.: A unified approach to interpreting model predictions (2017)
Demajo, L.M., Vella, V., Dingli, A.: Explainable AI for interpretable credit scoring. In: Computer Science & Information Tech-nology (CS & IT) (2020)
Troyanskaya, O., et al.: Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520–525 (2001)
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
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El Qadi, A., Trocan, M., Díaz-Rodríguez, N. et al. Feature contribution alignment with expert knowledge for artificial intelligence credit scoring. SIViP 17, 427–434 (2023). https://doi.org/10.1007/s11760-022-02239-7
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DOI: https://doi.org/10.1007/s11760-022-02239-7