保险公司经营状况评估与预测决策支持系统

Roman Panibratov
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引用次数: 0

摘要

决策支持系统是为了根据保险公司的财务和经济指标来估计和预测保险公司的状况而创建的。估计这类机构状态的任务被认为是一个二元分类问题:公司的活动是否有效。在研究过程中,实现了6种监督式机器学习方法:k近邻、支持向量机、朴素贝叶斯分类器、随机森林、XGBoost和深度神经网络。所创建的系统允许对财务和经济指标进行相关性分析,检查数据的平衡性,对所选模型进行训练并估计训练质量,根据所选模型预测保险公司的状态。根据最佳模型,对乌克兰保险公司的未来状况进行了预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Decision support system for estimating and forecasting state of insurance company
The decision support system was created for estimating and forecasting the state of an insurance company according to its financial and economic indicators. The task of estimating the state of this type of an institution was considered as a problem of a binary classification: whether the company’s activity is efficient or not. During the research, six supervised machine learning methods were implemented: k-nearest neighbors, support vector machine, naive Bayes classifier, random forest, XGBoost and deep neural network. The created system allows the following: to perform correlation analysis of financial and economic indicators, to check the balance of data, to perform training of the selected model and to estimate quality of training, to predict the state of the insurance company according to the selected model. According to the best model, the future state of insurance companies in Ukraine was predicted.
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