In the assurance field, being able to recognize the presence of damages in an object is a delicate task, that requires dedicated human resources in order to help customers while also preventing frauds. In this work, I present an innovative method for tackling this task using machine learning techniques such as Damage Detection and Image Re-Identification, specifically for the case of bike insurance. Given the general scarce availability of real images for this specific field, I also took advantage of purposely generated synthetic images in our training process. I performed a comprehensive study and research work on both the Damage Detection and Image Re-Identification fields, which led me in developing a pure Transformer-based Multi-Task network that is able to perform both tasks simultaneously while obtaining promising results in both, and demonstrating how both tasks can benefit from each other.