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In this paper, new models for credit risk prediction in Colombia are proposed by using different artificial intelligence techniques. These models can be used to support the risk management area in banks, and they aim to identify clients that could be in default, generating a possible credit risk for financial institutions. Three techniques are used to obtain the models (neuronal networks, decision trees, and support vector machines) that predict the next payment of a client's fee based on basic data from the client and previous recorded installment payments. Decision trees turns out to be more accurate than the other techniques that have been used when predicting credit risk with a ROC area of 88.29%. The proposed models reach accuracies that are like some other papers in the state of the art and in some cases, they overcome models in other studies. [ABSTRACT FROM AUTHOR]

In Colombia, social networks have become a powerful tool to disseminate political opinions, especially during the campaign period in the national presidential elections. This paper proposes a hybrid model to predict the outcome of the first round of presidential elections in Colombia in 2018, which aims to minimize absolute error and improve the quality of the final prediction. User activities on Twitter and Facebook were recorded and analyzed with artificial intelligence algorithms, resulting in an accurate prediction consistent with reality. As a core result is highlighted that the RMSE of the hybrid model is around 2.47%, surpassing on average the RMSE of the country's most prominent traditional polling firms. Additionally, the value of electoral abstentionism was also predicted with a differential error of 1.72% in relation to the real value, demonstrating the reliability of the proposed methodology. [ABSTRACT FROM AUTHOR]

In this paper is shown the application of neural networks in order to predict academic marks that will be obtained for the students in the subjects of Data Structures I and II, included both in the Informatics Engineering curricula at Higher Polytechnic Institute José Antonio Echeverría in the Republic of Cuba. The main motivation for the present work is justified because selected subjects have a high level of complexity, demanding from the student to be rigorous and a permanent dedication. As a consequence the academic results obtained at the present time are not satisfactory. To reach the goal mentioned above a software based on MATLAB tool was developed and the marks obtained previously by students in some subjects and others data of interest were used. Two neural networks were employed, both with the same architecture, but each one trained with the specific data of each subject (Data Structures I and II). A group of experiments was carried out to contrast the behavior of the neural networks regarding some specific statistics in the data of the sample. An overall effectiveness in prediction superior to 78% for the case of the first subject was achieved, while for the second one effectiveness superior to 75% was reached. [ABSTRACT FROM AUTHOR]

Every day organizations have more information because their systems produce a large amount of daily operations which are stored in transactional databases. In order to analyze this historical information, an interesting alternative is to implement a Data Warehouse. In the other hand, Data Warehouses are not able to perform predictive analysis for themselves, but machine learning techniques can be used to classify, grouping and predict historical information in order to improve the quality of analysis. This paper depicts architecture of a Data Warehouse useful to perform an analysis of students' academic performance. The Data Warehouse is used as input of a Neural Network in order to analyze historical information and forecast. The results show the viability of using Data Warehouse for academic performance analysis and the feasibility of predicting the number of approved courses for students using only their own historical information. [ABSTRACT FROM AUTHOR]