Risk Automatic Prediction for Social Economy Companies using Camels
This work addresses the challenge for government inspectors in efficiently monitoring SEEs by focusing on high-risk cases, though it is incremental as it applies an existing method to a new domain.
The paper tackles the problem of supervising social economy enterprises (SEEs) by predicting their future risk using a machine learning model, achieving 76% overall accuracy and identifying key predictors like legal nature and past-due portfolio variation.
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.