This work presents a strategy to create a large scale database for near duplicate image detection from insurance claims. The basic idea is semantically comparing the images in a database. When the database is expanded with new images, the possibility to detect near duplicates increases as well. Thus, performance of detecting the possible near duplicates is strictly related to how much the database is large. On the other hand, it is not always easy to expand a database with natural images especially when the new samples are received as raw data. The study is based on a large scale private dataset provided by an insurance company.