Insurance claim fraud is an escalating concern for Colombia’s insurance sector, particularly the Mandatory Traffic Accident Insurance, or SOAT. This study addresses this issue by applying supervised machine-learning techniques to enhance fraud detection and prevention. A comprehensive analysis of a historical dataset provided by Valuative SAS, which includes over one million claim records, evaluated multiple classification models, including Support Vector Machines, Random Forest, XG-Boost, and neural networks. The results demonstrate that the selected models can identify fraud patterns with high precision, offering significant potential to reduce financial losses and increase the sustainability of the SOAT insurance system in Colombia. This work proposes a replicable and scalable methodology to combat insurance fraud at both national and international levels.