标题： 基于机器学习的金融信用风险分析 显示英文标题

标题： Analysis of Financial Credit Risk Using Machine Learning

摘要： 企业破产可能对经济产生毁灭性的影响。 随着越来越多的公司到海外扩张以利用国外资源，跨国企业破产可能会扰乱全球金融生态系统。 企业不会立即倒闭；通过客观指标和对定性（例如品牌）和定量（例如经济计量因素）数据的严格分析，可以帮助识别公司的财务风险。 由于通信和信息技术的最新进展，收集和存储有关企业的数据变得更加容易。 剩下的挑战在于从大量数据中挖掘出有关公司健康状况的相关信息，并利用这些信息预测破产，以便管理者和利益相关者有时间做出反应。 近年来，机器学习因其在学习复杂模型方面的成功而在大数据分析领域变得非常流行。 支持向量机、自适应提升、人工神经网络和高斯过程等方法可以用于识别数据中的模式（具有很高的准确性），这些模式可能对人类分析师来说并不明显。 本论文分别使用专家意见和财务指标研究了韩国和波兰制造业公司的企业破产情况。 使用公开的数据集，应用了多种机器学习方法来学习公司当前状况与其近期命运之间的关系。 结果表明，当使用像专家评估这样的信息特征时，任何机器学习技术都可以实现超过95%的准确率的预测。 然而，当仅使用财务因素来预测公司是否会破产时，相关性就不那么强了。 显示英文摘要

摘要： Corporate insolvency can have a devastating effect on the economy. With an increasing number of companies making expansion overseas to capitalize on foreign resources, a multinational corporate bankruptcy can disrupt the world's financial ecosystem. Corporations do not fail instantaneously; objective measures and rigorous analysis of qualitative (e.g. brand) and quantitative (e.g. econometric factors) data can help identify a company's financial risk. Gathering and storage of data about a corporation has become less difficult with recent advancements in communication and information technologies. The remaining challenge lies in mining relevant information about a company's health hidden under the vast amounts of data, and using it to forecast insolvency so that managers and stakeholders have time to react. In recent years, machine learning has become a popular field in big data analytics because of its success in learning complicated models. Methods such as support vector machines, adaptive boosting, artificial neural networks, and Gaussian processes can be used for recognizing patterns in the data (with a high degree of accuracy) that may not be apparent to human analysts. This thesis studied corporate bankruptcy of manufacturing companies in Korea and Poland using experts' opinions and financial measures, respectively. Using publicly available datasets, several machine learning methods were applied to learn the relationship between the company's current state and its fate in the near future. Results showed that predictions with accuracy greater than 95% were achievable using any machine learning technique when informative features like experts' assessment were used. However, when using purely financial factors to predict whether or not a company will go bankrupt, the correlation is not as strong.