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Other forms of machine learning include the ability to group or “cluster” like items. Items are grouped together based upon a selection of screening characteristics. Taking the above invoice item example, a cluster of like items could be created based upon a single characteristic, item description, or multiple characteristics for example including industry. The tax rate for items in a group could then be examined and outliers from the group norm identified for investigation. This approach can work well to identify outliers even when items are grouped based upon many characteristics.

Another machine learning approach worth mentioning is social network analysis. This approach enables measurement of the strength of association between two parties. It provides a means of detecting networks of actors working in concert to accomplish a variety of frauds. While it should be applied carefully as guilt by association is a slippery slope, when used correctly it can play a role in protecting against multiparty organized fraud like missing trader and fake invoice schemes.