The present paper aims to compare the predictive performance of five models namely the Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Trees (DT), Support Vector Machine (SVM) and Random Forest (RF) to forecast the bankruptcy of Tunisian companies. A Deep Neural Network (DNN) model is also applied to conduct a prediction performance comparison with other statistical and machine learning algorithms. The data used for this empirical investigation covers 25 financial ratios for a large sample of 732 Tunisian companies from 2011–2017. To interpret the prediction results, three performance measures have been employed; the accuracy percentage, the F1 score, and the Area Under Curve (AUC). In conclusion, DNN shows higher accuracy in predicting bankruptcy compared to other conventional models, whereas the random forest performs better than other machine learning and statistical methods.

中文翻译：

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突尼斯公司破产预测的人工智能技术：机器学习和深度学习模型的应用

本文旨在比较线性判别分析（LDA）、逻辑回归（LR）、决策树（DT）、支持向量机（SVM）和随机森林（RF）五种模型对破产企业预测的预测性能。突尼斯公司。深度神经网络（DNN）模型还用于与其他统计和机器学习算法进行预测性能比较。本次实证调查使用的数据涵盖 2011 年至 2017 年 732 家突尼斯公司的大样本的 25 个财务比率。为了解释预测结果，采用了三种性能指标；准确率、F1 分数和曲线下面积 (AUC)。总之，与其他传统模型相比，DNN 在预测破产方面表现出更高的准确性，而随机森林则比其他机器学习和统计方法表现更好。