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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.

An innovative platform capable of interpreting and processing data from all types of documents, including paper (scanned or photographed) or digital, structured and unstructured documentation formats, images, email attachments and message bodies.

Thanks to the intelligent digitization of documents and the automation of document processes , it transforms documents into value for the company, capturing and validating information in any format at the point of use.

An Artificial Intelligence platform that deals with analyzing images and documents of any structure and complexity to recognize graphic manipulations, visual inconsistencies, verify the uniqueness of the image within the DB, check that the image is not present on the internet. To combat insurance fraud related to false documents.

Artificial Intelligence, Computer vision and Object Recognition to recognize the damaged parts of the photographed vehicle, distinguish between heavy and light damage and make an initial estimate of the repair both in terms of costs and times, channeling complex and mass claims in a totally automatic way .

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.