Application of Decision Tree to Banking Classification Model

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. 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 Random Forest, 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 new process will guide the organization to implement these models in other areas such as risk, finance, auditing, and operations.