This paper presents an automatic prediction system using a neural network for risks of an international financial institution-funded project. In test stage, 3-fold cross validation method was applied to the Accountability Counsel and World Bank datasets to evaluate the system performances using seven indicators (Micro and Macro-Averaged Precision, Recall, F1 score, and Hamming loss). Experimental results show that the Hamming loss, micro- and macro- precision, recall, and F1 score of multilabel method were at best 0.06%, 88.10%, 72.57%, 99.96%, 99.71%, 93.45% and 58.34% respectively. This research demonstrated that this neural network can be used for reducing the dimension of feature space and that the proposed model is a promising application as an efficient automatic risk classification system for potential risks of IFI−financed projects.

supervised learning, multilabel, classification, regression, neural network, automatic detection, public sector investment, international finance

Feel free to read the report, download the code, or test the model.

CS221 Poster Fair, Autumn 2018.

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