Prediction of applicants who will commit internal fraud in a company using supervised learning algorithms

• Autor: Espinoza-Montalvo, Sergio
• Formato: artículo
• Fecha de Publicación: 2019
• Institución: Universidad de Lima
• Repositorio: Revistas - Universidad de Lima
• Lenguaje: español
• OAI Identifier: oai:revistas.ulima.edu.pe:article/4637
• Enlace del recurso: https://revistas.ulima.edu.pe/index.php/Interfases/article/view/4637
• Nivel de acceso: acceso abierto
• Materia: Supervised learning; fraud prediction; antisocial personality disorder; internal fraud; Aprendizaje supervisado; predicción de fraude; trastorno antisocial; fraude interno

Internal fraud is a big problem for companies since it causes significant monetary losses. Several research studies have proposed to improve the personnel selection process using data mining. The present work suggests to use applicants’ historical information in order to predict if they will commit fraud during their working period in a company. There are models with high precision level but with a higher error rate to find fraud. After several ex­perimentations, around seven variables which contribute more to the model were found. Some of these variables match those mentioned in studies about antisocial personality disorder. The algorithm with best results was a convolutional neural network with 80% accuracy rate. It is concluded that applicants’ information is important to establish if they will commit internal fraud during their working period in a company.