Classification

Automatic Classification is strongly based on the scientific discipline known as supervised machine learning, which consists in presenting labelled training data from past classifications, provided by experts on the topic, to a system of classification. This system analyses the training data and, using statistical learning algorithms, infers the knowledge of the expert, thus imitating their work.

All this learning process results in a classification system with capacity to process a high volume of data and have shorter response times than those of a manual management. Besides, the system is alive and can be reloaded with new data to readjust the classification processes. This way, we can make sure it is updated to new business policies or future trends on the data.

Exploitation of these classification systems is made applying the business rules to the classifications generated by them. For each classification, the system generates a certainty score that indicates the confidence the system has in its own decision. This allows setting business rules such as automatically issuing an insurance policy classified as low-risk with a high score, or deriving others which generate more doubts to an expert analyst. This means reducing the workload of experts substantially, or increasing the volume of data processed counting on the same number of employees.