论文标题
使用骆驼对社会经济公司的风险自动预测
Risk Automatic Prediction for Social Economy Companies using Camels
论文作者
论文摘要
政府必须监督和检查社会经济企业（SEE）。但是，由于大量的距离，检查员数量少，因此无法检查所有看到。我们提出了一个基于机器学习方法的预测模型。使用随机森林算法训练该方法，并提供了每个See提供的历史数据。连续三个数据串联了三个时期。所提出的方法将这些时期用作输入数据，并预测第四个时期内看到的风险。该模型达到了76 \％的总体精度。此外，它在预测A See的高风险方面获得了良好的准确性。我们发现，法律的性质和过去的投资组合的变化是对未来风险的良好预测指标。因此，可以通过有监督的机器学习方法预测将来的观察风险。通过仅专注于高风险的SEE，预测A See的高风险可以改善每个检查员的日常工作。
Governments have to supervise and inspect social economy enterprises (SEEs). However, inspecting all SEEs is not possible due to the large number of SEEs and the low number of inspectors in general. We proposed a prediction model based on a machine learning approach. The method was trained with the random forest algorithm with historical data provided by each SEE. Three consecutive periods of data were concatenated. The proposed method uses these periods as input data and predicts the risk of each SEE in the fourth period. The model achieved 76\% overall accuracy. In addition, it obtained good accuracy in predicting the high risk of a SEE. We found that the legal nature and the variation of the past-due portfolio are good predictors of the future risk of a SEE. Thus, the risk of a SEE in a future period can be predicted by a supervised machine learning method. Predicting the high risk of a SEE improves the daily work of each inspector by focusing only on high-risk SEEs.
