Corporate insolvency has significant adverse effects on an economy. With the number of multinationals increasing rapidly, corporate bankruptcy can severely disrupt the global financial environment. However, multinationals do not fail instantaneously; objective strategies combined with a rigorous analysis of both qualitative and quantifiable data can go a long way in identifying an organization's financial risks. Recent advancements in information and communication technologies have made data collection and storage an easy task. The challenge becomes mining the appropriate data about a company's financial risks and implementing it in forecasting a company's insolvency probabilities. In recent years, machine learning has been incorporated into big data analytics owing to its massive success in learning complex models. Machine learning algorithms such as Support Vector Machines (SVM), Random Forests (RF), Artificial Neural Networks, Gaussian Processes, and Adaptive Learning have been used in the analysis of Big Data to predict the financial risks of companies. In this paper, credit scoring is explored with regards to data processed using the collateral as an independent variable. The obtained results indicate that RF algorithm is promising for use in credit risk management. This research shows the advantages of the RF approach over the SVM algorithm are its speed and operational simplicity, and SVM has the benefit of higher classification accuracy than RF. The paper compares the SVM and RF algorithms to forecast the recovered value in a credit task. The execution of the projected intelligent systems uses tests and algorithms for authentication of the projected model.

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支持向量机与随机森林机器学习算法对信用操作的比较研究

公司破产对经济产生重大不利影响。随着跨国公司数量的迅速增加，企业破产会严重扰乱全球金融环境。然而，跨国公司不会立即失败。客观战略与对定性和量化数据的严格分析相结合，可以在识别组织的财务风险方面大有帮助。信息和通信技术的最新进展使数据收集和存储成为一项轻松的任务。挑战在于挖掘有关公司财务风险的适当数据并在预测公司破产概率时实施这些数据。近年来，由于机器学习在学习复杂模型方面的巨大成功，它已被纳入大数据分析。支持向量机 (SVM)、随机森林 (RF)、人工神经网络、高斯过程和自适应学习等机器学习算法已被用于大数据分析以预测公司的财务风险。在本文中，关于使用抵押品作为自变量处理的数据来探讨信用评分。所得结果表明，RF 算法有望用于信用风险管理。这项研究表明，RF 方法相对于 SVM 算法的优势在于其速度和操作简单性，并且 SVM 具有比 RF 更高的分类精度的优点。该论文比较了 SVM 和 RF 算法在信用任务中预测恢复值。