Risk Automatic Prediction for Social Economy Companies using Camels

TL;DR

This paper presents a machine learning model using random forest to predict the future risk of social economy enterprises, helping inspectors prioritize high-risk companies efficiently.

Contribution

It introduces a novel predictive approach utilizing historical data and three periods of information to accurately assess SEE risks, enhancing inspection efficiency.

Findings

Achieved 76% overall accuracy in risk prediction

Legal nature and past-due portfolio variation are key predictors

Effective identification of high-risk SEEs for inspection prioritization

Abstract

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.