Explainable fintech lending

Babaei, Golnoosh; Giudici, Paolo; Raffinetti, Emanuela

doi:10.2139/ssrn.3892652

Cited by 3 publications

(2 citation statements)

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“…Later, some studies used the Shapley values to measure the contribution of variables in the model to the target variable. Babaei et al [25] applied machine learning to predict default for small and medium enterprises. The authors eliminated variables with low explanatory Sharley values.…”

Section: Literature Review On Explanationmentioning

confidence: 99%

Explainable Machine Learning for Financial Distress Prediction: Evidence from Vietnam

Tran

Nguyen

et al. 2022

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The past decade has witnessed the rapid development of machine learning applied in economics and finance. Recent evidence suggests that machine learning models have produced superior results to traditional statistical models and have become the driving force for dramatic improvement in the financial industry. However, a much-debated question is whether the prediction results from black box machine learning models can be interpreted. In this study, we compared the predictive power of machine learning algorithms and applied SHAP values to interpret the prediction results on the dataset of listed companies in Vietnam from 2010 to 2021. The results showed that the extreme gradient boosting and random forest models outperformed other models. In addition, based on Shapley values, we also found that long-term debts to equity, enterprise value to revenues, account payable to equity, and diluted EPS had greatly influenced the outputs. In terms of practical contributions, the study helps credit rating companies have a new method for predicting the possibility of default of bond issuers in the market. The study also provides an early warning tool for policymakers about the risks of public companies in order to develop measures to protect retail investors against the risk of bond default.

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Section: Literature Review On Explanationmentioning

confidence: 99%

Explainable Machine Learning for Financial Distress Prediction: Evidence from Vietnam

Tran

Nguyen

et al. 2022

Data

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“…The challenge being that regulatory demands require interpretability as a necessary precondition. In this vein, Giudici and Raffinetti (2022) address cyber risk management, Babaei et al (2023) the lending process within a fintech universe for small end medium enterprises and Babaei et al (2022) provide a discussion on asset allocation and crypto assets. 3 Up to now, recent literature on ML is mainly focused on classification problems and the assessment of individual feature contributions, especially when the target variable of the assessment is measured in a metric scale, is of growing interest not only in a financial context (see De Bock 2023.…”

Section: Introductionmentioning

confidence: 99%

On the information content of explainable artificial intelligence for quantitative approaches in finance

Berger

2024

OR Spectrum

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We simulate economic data to apply state-of-the-art machine learning algorithms and analyze the economic precision of competing concepts for model agnostic explainable artificial intelligence (XAI) techniques. Also, we assess empirical data and provide a discussion of the competing approaches in comparison with econometric benchmarks, when the data-generating process is unknown. The simulation assessment provides evidence that the applied XAI techniques provide similar economic information on relevant determinants when the data generating process is linear. We find that the adequate choice of XAI technique is crucial when the data generating process is unknown. In comparison to econometric benchmark models, the application of boosted regression trees in combination with Shapley values combines both a superior fit to the data and innovative interpretable insights into non-linear impact factors. Therefore it describes a promising alternative to the econometric benchmark approach.

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