The area of corporate bankruptcy prediction attains high economic importance, as it affects many stakeholders. The prediction of corporate bankruptcy has been extensively studied in economics, accounting and decision sciences over the past two decades. The corporate bankruptcy prediction has been a matter of talk among academic literature and professional researchers throughout the world. Different traditional approaches were suggested based on hypothesis testing and statistical modeling. Therefore, the primary purpose of the research is to come up with a model that can estimate the probability of corporate bankruptcy by evaluating its occurrence of failure using different machine learning models. As the dataset was not well prepared and contains missing values, various data mining and data pre-processing techniques were utilized for data preparation. Within this research, the task of resolving the issues induced by the imbalance between the two classes is approached by applying different data balancing techniques. We address the problem of imbalanced data with the random undersampling and Synthetic Minority Over Sampling Technique (SMOTE). We used five machine learning models (support vector machine, J48 decision tree, Logistic model tree, random forest and decision forest) to predict corporate bankruptcy earlier to the occurrence. We use data from 2009 to 2013 on Poland manufacturing corporates and selected the 64 financial indicators to be broken down. The main finding of the study is a significant improvement in predictive accuracy using machine learning techniques. We also include other economic indicators ratios, along with Altman’s Z-score variables related to profitability, liquidity, leverage and solvency (short/long term) to propose an efficient model. Machine learning models give better results while balancing the data through SMOTE as compared to random undersampling. The machine learning technique related to decision forest led to 99% accuracy, whereas support vector machine (SVM), J48 decision tree, Logistic Model Tree (LMT) and Random Forest (RF) led to 92%, 92.3%, 93.8% and 98.7% accuracy, respectively, with all predictive financial indicators. We find that the decision forest outperforms the other techniques and previous techniques discussed in the literature. The proposed method is also deployed on the web to assist regulators, investors, creditors and scholars to predict corporate bankruptcy.

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公司破产预测：通向更好的公司世界的方法

公司破产预测领域具有很高的经济重要性，因为它影响到许多利益相关者。在过去的二十年中，对公司破产的预测已在经济学，会计学和决策科学领域进行了广泛的研究。公司破产的预测一直是全世界学术文献和专业研究人员的话题。基于假设检验和统计模型，提出了不同的传统方法。因此，研究的主要目的是提出一个模型，该模型可以通过使用不同的机器学习模型评估企业破产的可能性来估计企业破产的可能性。由于数据集准备不充分且包含缺失值，各种数据挖掘和数据预处理技术被用于数据准备。在这项研究中，通过应用不同的数据平衡技术来解决由两类之间的不平衡引起的问题。我们使用随机欠采样和合成少数采样率（SMOTE）解决数据不平衡的问题。我们使用了五种机器学习模型（支持向量机，J48决策树，Logistic模型树，随机森林和决策森林）来预测公司破产发生的时间。我们使用2009年至2013年波兰制造业公司的数据，并选择了要细分的64个财务指标。该研究的主要发现是使用机器学习技术的预测准确性的显着提高。与获利能力，流动性，杠杆和偿付能力（短期/长期）相关的Z得分变量，以提出有效的模型。与随机欠采样相比，机器学习模型在通过SMOTE平衡数据的同时提供了更好的结果。与决策森林相关的机器学习技术导致了99％的准确性，而支持向量机（SVM），J48决策树，逻辑模型树（LMT）和随机森林（RF）导致了92％，92.3％，93.8％和98.7所有预测性财务指标的准确度分别为％。我们发现决策林的性能优于文献中讨论的其他技术和以前的技术。提议的方法还可以在网络上部署，以帮助监管机构，投资者，债权人和学者预测公司破产。