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Governments have to supervise and inspect social economy enterprises (SEEs).However, inspecting all SEEs is not possible due to the large number of SEEsand the low number of inspectors in general. We proposed a prediction modelbased on a machine learning approach. The method was trained with the randomforest algorithm with historical data provided by each SEE. Three consecutiveperiods of data were concatenated. The proposed method uses these periods asinput data and predicts the risk of each SEE in the fourth period. The modelachieved 76\% overall accuracy. In addition, it obtained good accuracy inpredicting the high risk of a SEE. We found that the legal nature and thevariation of the past-due portfolio are good predictors of the future risk of aSEE. Thus, the risk of a SEE in a future period can be predicted by asupervised machine learning method. Predicting the high risk of a SEE improvesthe daily work of each inspector by focusing only on high-risk SEEs.

Risk Automatic Prediction for Social Economy Companies using Camels