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Automation through artificial intelligence brings with it a significant opportunity to optimize decision-making processes and reduce management costs and is therefore destined to permeate all aspects of the insurance business, allowing employees and organizations to focus more on business. of greater value as well as on works that require more imagination and creativity.

The assignment of repetitive and low-added-value activities to “intelligent” tools and technologies not only leads to an improvement in process efficiency, but above all ensures compliance with increasingly stringent regulatory requirements for verifiability, security, data quality and operational resilience .

ICG supports companies in exploiting the potential of Artificial Intelligence to implement sector-specific solutions. Thanks to the skills consolidated during the implementation of numerous PoCs with latest generation technologies, it can be considered at the forefront of these issues that will inevitably mark the near future of the insurance world.

Un’innovativa piattaforma in grado di interpretare ed elaborare dati provenienti da tutti i tipi di documenti, inclusi formati di documentazione cartacea (acquisita tramite scanner o fotografia) o digitale, strutturati e non strutturati, immagini, allegati email e corpo dei messaggi.

Grazie alla digitalizzazione intelligente dei documenti e all’automatizzazione dei processi documentali, trasforma i documenti in valore per l’azienda, catturando e convalidando informazioni in qualsiasi formato nel punto di impiego.

Una piattaforma di Intelligenza Artificiale che si occupa di analizzare immagini e documenti di qualsiasi struttura e complessità per riconoscere manipolazioni grafiche, incoerenze visive, verificare l’univocità dell’immagine all’interno del DB, controllare che l’immagine non sia presente in internet. Per combattere le frodi assicurative legate a falsi documentali.

Intelligenza Artificiale, Computer vision e Object Recognition per riconoscere le parti danneggiate del veicolo fotografato, distinguere tra danni pesanti e leggeri ed effettuare una iniziale stima della riparazione sia in termini di costi che di tempi, canalizzando in maniera totalmente automatica sinistri complessi e sinistri di massa.

In order for this process to be efficient, it is necessary to “train” the neural networks, that is to make them learn how to behave when an engineering problem has to be solved, such as the recognition of a human being from the analysis of images (through for example facial recognition technology).

A neural network actually looks like an “adaptive” system capable of modifying its structure (nodes and interconnections) based on both external data and internal information that connect and pass through the neural network during the learning phase and reasoning.

The revolution brought about by Deep Learning is evident on many fronts, so much so that starting from 2015 algorithms that exploit deep neural networks have exceeded the capabilities of man in simple tasks such as image recognition and transcription of audio into text.

The merits of Deep Learning are to be found in the ability to obtain ever better results by increasing the complexity of the neural network or by adding unstructured input data to the model. From the point of view of performance, the possibility of exploiting GPUs for massive computations has also favored the introduction of more complicated algorithms in production environments.