Receipt fraud detection
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