The project is developed in the scope of a digital insurance product. The aim of the AI project is to develop an algorithm to identify a number of furniture elements and from this detection create a referential price of the content identified.

The project is an application that takes an image or a stream. This input is then processed and analyzed by a Deep Learning model. The model used is YOLO v3. Once processing is finished, the output returned by the algorithm is parsed and analyzed. Each element detected is compared with a price table. Finally, after all elements are identified, a summary report is generated with the number of the different elements identified and the total cost of the content detected.

Business Value

Improve the user experience:
– Easy way to encode the content to insure
– Give the possibility to edit the content detected
– The client can receive a personalized offer based on the value of the content to insure
– Automatic report generated summarizing the elements to insure and its value

For the insurer the Digitalization of insurance products open the possibility to acquire new type of clients.

Implementation Plan

– Functional and technical analysis
– Data acquisition
– Development of the Deep Learning model
– Train/ test the model
– Create the application around the model itself
– Setup the REST API system
– Testing
– Deployment of the model on a cloud infrastructure

Commercial representation of YOLO output
Used technologies
Effort Needed
20 mandays (PoC) and 60 mandays (Prod)
Diversification in Insurance leads to new markets. The target of a full digital insurance is millennials
Insurance company
Application Type
Image recognition and location
Tech Stack
– Python
– Jupyter
– Pytorch
– OpenCV
Learning Path