INTERNAL FRAUD DETECTION IN FINANCIAL INSTITUTIONS USING MACHINE LEARNING TECHNIQUES

Correa J.; Rojas S. · Journal of Theoretical and Applied Information Technology (2026)

Resumen

This research aimed to implement a machine learning model aligned with Sustainable Development Goal No. 12: Responsible Consumption and Production, which focuses on strengthening access to information and technological knowledge. The objective of this study was to develop a machine learning model to enhance the detection of internal fraud within a financial institution located in Sullana, Piura, Perú. This was an applied research study, employing a quantitative approach, a pre-experimental design, and a descriptive scope. The population consisted of 6000 operational records from the financial institution and 25 employees from the business department. Observation guides and questionnaires were used as data collection instruments during the execution of the project. The results showed a 37% reduction in unusual behaviors, a 50-minute decrease in the average detection time, a 27.5% increase in accuracy, and a 96% rate of high employee perception. These findings indicate that the machine learning model proved to be an effective tool for fostering a culture of transparency, thereby reducing operational risks and reinforcing trust in the local financial system. © Little Lion Scientific

Instituciones: Universidad César Vallejo