Machine learning aplicado en la medición de riesgo de seguros de embarques de una consolidadora de carga en Guayaquil en 2019.

Abstract

Within the vast world of foreign trade, it is common knowledge that they are those who manage the processes for the import and export of products. Both in Ecuador and in several countries of the world they present inconveniences of some kind, whether at the operational, administrative, logistical level, among others. Certain ports are more advanced than others in terms mentioned above, therefore, troubleshooting may be late or even troubleshooting would be efficient. With the use of machine learning, in a world with a great influence in the technological sector, the processes of transporting a ship from origin to destination port can be continued easily and efficiently. Insurance is a key factor, since it is responsible for providing coverage for containerized cargo during the journey to its final destination. Within the present work, the key factors that negatively influence the cargo transported from various ports around the world to the local port in Guayaquil will be revealed. Knowing the incidents and inconveniences in containerized cargo, at an academic level, solutions could be contemplated or created to minimize or eradicate them. In turn, within the business environment, it allows the implementation of ideas for prompt and effective execution. In addition, within a pandemic context, in which the cargo suffered delays, damage and even losses due to the inefficient supply chain as a result of massive layoffs concatenating a health crisis. The future is the product of the processes that are carried out in the present, therefore, the implementation of machine learning models for companies will provide facilities and automation as well as the development of ideas or their execution for future problems previously created.