ESTIMATING THE RISK OF VEHICLE CRIME OCCURRENCE IN SÃO PAULO STATE USING NEURAL NETWORKS

Resumo

DOI:https://doi.org/10.5281/zenodo.10719541

The increasing number of vehicle-related crimes and rising costs of financial services are impacting the level of customization offered to vehicle insurance customers. To address this issue, this study proposes a platform that leverages machine learning and neural networks to analyze data from the São Paulo State Secretariat of Public Security (SSP) and the Brazilian Institute of Geography and Statistics (IBGE). The platform aims to provide a more accurate assessment of an insured individual's risk profile based on personal information, such as city and neighborhood of residence.