Task: to increase the speed of finding fraudulent ads. Employees of the anti-fraud department noticed that they often set up filters in a specific way for each district of the city, taking into account the height, novelty of the building, proximity to the subway, etc. all to find atypical ads. This procedure takes a lot of time and requires highly qualified employees, but the popularity of the service has increased in recent years and the department needed search automation.
We analyzed the data to detect anomalies and built a model that combines a tree ensemble and regression, which itself finds potential outliers and questionable price values for the proposed conditions. It turned out that the model is good to observe changes in real estate prices. Over time, we finalized it and it began to participate in pricing. Two birds with one stone!