La inteligencia artificial se encuentra en constante evolución y se está aplicando en varias áreas, dentro de esta se encuentra la educación. El análisis del rendimiento académico de los estudiantes en instituciones de educación superior es un tema crítico para la toma de decisiones y el mejoramiento de la calidad de la educación. El objetivo de este artículo es realizar una revisión sistemática de la literatura, considerando las investigaciones que se han desarrollado utilizando técnicas de inteligencia artificial para analizar el rendimiento académico en instituciones de educación superior. Se han considerado las bases de datos científicas Web of Science, Scopus, e IEEE Xplore. Se consideró palabras clave relacionadas con la inteligencia artificial y el rendimiento académico. Se tomó en cuenta artículos publicados desde enero 2017 hasta diciembre 2022, se obtuvieron 1427 manuscritos, de los cuales se seleccionaron y analizaron 74, según los criterios de inclusión y exclusión predefinidos. Entre los resultados obtenidos se puede indicar que las técnicas más utilizadas para la predicción del rendimiento académico son: las redes neuronales y los árboles de decisión. En conclusión, se puede indicar que la aplicación de la inteligencia artificial puede mejorar la eficiencia y precisión de la evaluación, y proporcionar información valiosa para la toma de decisiones y la mejora de la calidad de la educación. Adicionalmente, se discuten las implicaciones y limitaciones de estos estudios y se proponen áreas para futuras investigaciones.

Purpose: One of the key elements in the banking industry relies on the appropriate selection of customers. To manage credit risk, banks dedicate special efforts to classify customers according to their risk. The usual decision-making process consists of gathering personal and financial information about the borrower. Processing this information can be time-consuming, and presents some difficulties because of the heterogeneous structure of data. Design/methodology/approach: This paper presents an alternative method that is able to generate rules that work not only on numerical attributes but also on nominal ones. The key feature of this method, called learning vector quantization and particle swarm optimization (LVQ + PSO), is the finding of a reduced set of classifying rules. This is possible because of the combination of a competitive neural network with an optimization technique. Findings: These rules constitute a predictive model for credit risk approval. The reduced quantity of rules makes this method useful for credit officers aiming to make decisions about granting a credit. It also could act as an orientation for borrower’s self evaluation about her/his creditworthiness. Research limitations/implications: In spite of the fact that conducted tests showed no evidence of dependence between results and the initial size of the LVQ network, it is considered desirable to repeat the measurements using an LVQ network of minimum size and a version of variable population PSO to adequately explore the solution space in the future. Practical implications: In the past decades, there has been an increase in consumer credit. Retail banking is a growing industry. Not only has there been a boom in credit card memberships, specially in emerging economies, but also an increase in small consumption credits. For example, it is very common in emerging economies that families buy home appliances on installments. In those countries, the association of a home appliance shop with a financial institution is usual, to provide customers with quick-decision credit line facilities. The existence of such a financial instrument aids to boost sales. This association generates conflict of interests. On one hand, the home appliance shop wants to sell products to all customers. Therefore, it is in its best interest to promote a generous credit policy. On the other hand, the financial institution wants to maximize the revenue from credits, leading to a strict surveillance of loan losses. Having a fair and transparent credit-granting policy favors a good business relationship between home appliances shops and financial institutions. One way of developing such a policy is to construct objective rules to decide to grant or deny a credit application. Social implications: Better credit decision rules generate enhanced risk sharing. In addition, it improves transparency in credit acceptance decisions, giving less room to arbitrary decisions. Originality/value: This study develops a new method that combines a competitive neural network and an optimization technique. It was applied to a real database of a financial institution in a developing country.

Knowledge generated using data mining techniques is of great interest for organizations, as it facilitates tactical and strategic decision making, generating a competitive advantage. In the special case of credit granting organizations, it is important to clearly define rejection/approval criteria. In this direction, classification rules are an appropriate tool, provided that the rule set has low cardinality and that the antecedent of the rules has few conditions. This paper analyzes different solutions based on Particle Swarm Optimization (PSO) techniques, which are able to construct a set of classification rules with the aforementioned characteristics using information from the borrower and the macroeconomic environment at the time of granting the loan. In addition, to facilitate the understanding of the model, fuzzy logic is incorporated into the construction of the antecedent. To reduce the search time, the particle swarm is initialized by a competitive neural network. Different variants of PSO are applied to three databases of financial institutions in Ecuador. The first institution specializes in massive credit placement. The second institution specializes in consumer credit and business credit lines. Finally, the third institution is a savings and credit cooperative. According to our results, the incorporation of fuzzy logic generates rule sets with greater precision.