Predicción de postulantes que cometerán fraude interno con algoritmo de aprendizaje supervisado

• Autor: Espinoza Montalvo, Sergio Ernesto
• Formato: tesis de grado
• Fecha de Publicación: 2020
• Institución: Universidad de Lima
• Repositorio: ULIMA-Institucional
• Lenguaje: español
• OAI Identifier: oai:repositorio.ulima.edu.pe:20.500.12724/12355
• Enlace del recurso: https://hdl.handle.net/20.500.12724/12355
• Nivel de acceso: acceso abierto
• Materia: Data mining; Fraud; Employee selection; Míneria de datos; Fraude; Selección de personal; https://purl.org/pe-repo/ocde/ford#2.02.04

Internal fraud is a big issue for companies, resulting in relevant monetary losses. Several investigations have proposed improvements to the personnel selection process making using of Data Mining. The present work proposes to use past information of applicants to a company to predict if they will commit fraud during their stay. We find models with high precision, but that have a bigger classification error to find the fraud cases. After several experiments, we find around 13 features of this universe that are most relevant to the model. Some of these features match with features mentioned in literature about antisocial disorders. We conclude that there is value in applicant information to predict if they will commit internal fraud during their stay in the company.