Diseño e implementación de soluciones de automatización para auditoría interna bancaria: NLP para SQR y validación de calidad de datos

Abstract

During my professional internship, I developed two projects aimed at improving internal processes and customer experience at a bank. The first project involved classifying and analyzing customer complaints and claims, using machine learning techniques to automate case categorization and detect relevant patterns. Through natural language processing (NLP) and classification algorithms, I identified critical areas for improvement and opportunities to enhance the bank's services. This project made a significant contribution to my training as a Telecommunications Engineering student, allowing me to apply my knowledge of processing, analyzing, and managing large volumes of data—fundamental skills in the field of information technology. The second project focused on data origination, specifically cross-referencing and verifying the information provided by customers. A process was designed to evaluate the quality of the recorded data in order to guarantee its integrity, consistency, and reliability. This initiative contributed to optimizing validation processes and reducing errors in information management, thus strengthening the entity's operational efficiency. Both projects allowed for the application of theoretical knowledge in a real-world environment, the development of analytical skills, and the proposal of practical solutions to common problems in the financial sector.