Receipt fraud detection I'm developing an OCR service that scans receipts and assigns points to user, based on amount of money spent. But the problem is that user can forge fake receipts and redeem them for extra points. User takes picture of receipt in app, which sends it to server, so it's not possible for user to modify picture after taking it. But user can print modified receipt (for example with changed receipt id) and take a picture of it. Is there a data science approach to solve this problem?
This is an anomaly detection problem. In your case, we would refer to it as supervised anomaly detection problem as you have the labels of categories.
This typically involves taking a large &quot;normal&quot; dataset, in this case, this would be receipts which are valid. And then using a machine learning method to learn features from this dataset (e.g. the typical words used, normal amounts entered, etc.) to generate a model of the &quot;normal&quot; data.
You could either go down the Computer Vision direction (e.g. for physical features in the photos) or down the Natural Language direction (e.g. for textual features in the receipts), or perhaps any other features available!
Once you have this model of &quot;normal&quot; behaviour, you can then test it against fraudulent receipts by predicting whether the receipt is normal.
But remember, the model will only be as good as your catalogue of &quot;normal&quot; and &quot;abnormal&quot; data