Application of Decision Tree to Banking Classification Model

Abstract: In this research, we will focus on INSOTEC NGO, an entity dedicated to granting microcredits to entrepreneurs with limited economic resources. This company is present in rural areas of Ecuador, increasing its income in recent years. The organization plans to become a bank in the long term and expand its operations to near countries such as Colombia and Peru. However, the entity's customer classification processes have had many drawbacks because it is currently a manual procedure that generates a high operational burden, slow response times to customers, huge inefficiency rates, and a great problem to continue growing. Therefore, the process is no longer sustainable as long as the organization continues growing. Competitors in the banking sector have implemented artificial intelligence projects with machine learning classification methods such as neural networks, decision trees, etc. This has led them to improve efficiency, increase reliability, reduce costs, and decrease the operating burden. These banks have taken advantage of these technological advances to reduce credit risk, control bank fraud such as money laundering, improve their marketing campaigns, and enhance their products and services to be more attractive and competitive in the market. INSOTEC plans to emulate these advances in customer rating processes to take advantage of the benefits mentioned above. This project proposes to model an artificial intelligence algorithm that classifies the organization's clients based on the different variables that are considered convenient for the analysis. The method selected to meet this objective is a decision tree, a supervised learning method that builds models that are easy to interpret. Its implementation complexity is very low, it allows continuous and categorical values, and it handles noise from data from different sources very well. This investigation has the right to use the information of the organization that is hosted in a SQL SERVER 2016 database, which contains the daily transactional details of the current portfolio, the default of loans, and the information of the main client: age, gender, credit range and type of products granted. Also, the dataset has historical data for the last three years but could include preliminary information if necessary. Model evaluation is an essential element of the investigation. In this case, a confusion matrix is a method selected to evaluate the results, it is expected to obtain a level of precision greater than 95%. This evaluation method is used to find the number of false positives to ensure the reliability of the model. The model must have high precision because if it fails, loans could be delivered to people who are not creditworthy and cannot meet the payments, which would be counterproductive for the organization since it would increase the default of loans and operating costs. In short, the machine learning model will automatically classify customers according to the different variables using a decision tree. This implementation will reduce the operational burden, response times to customers, and improve the competitiveness of the organization. This new process will guide the organization to implement these models in other areas such as risk, finance, auditing, and operations.

Keywords: Application, Decision Tree, Banking, Classification Model

DOI: 10.54941/ahfe1001130

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