Model for detecting false alarms in SMEs with Alarm Monitoring, through the adoption of droneand ANN technology to improve theircompetitiveness and customer satisfaction

Abstract

The increase in the perception of insecurity has generated that more and more people acquire an alarm system to safeguard their homes or businesses, to a Company with Central Monitoring (ECM), a recurring problem that occurs in these companies are false alarms (FA) generated by the user, the equipment or weather conditions, which on average are 89% according to the National Security Commission; these false alarms generated in 2018 a cost for the Mexican State of $82,431,852 million dollars; besides generating a perception in customers of a bad service that generates dissatisfaction and the State due to the high losses already began to Legislate on this issue and the sanctions range from fines to revocation of the service license; so it is important to minimize these FA since they endanger the existence of these companies. This research project aims to develop a model, through a neural network (ANN) that performs a data analysis to detect the FA and through the use of a drone to verify the FA, so that the ECM are more competitive and improve customer satisfaction by innovating its monitoring process and the perception of quality service.