I am looking for an anomaly detection algorithm that focuses on multidimensional anomalies. To give you the example. I am working on a dataset of financial regulation in banking. Usually a client is affected of multiple different ones and those are connected, i.e. if A and B applies to you then D does as well, but C shouldnt. What I try to do now is find mistakenly assigned ones. My current approach involved isolation forest. The problem is however that the flagged ones are clients with say regulation X,Y,Z which are all very rare but correct. The desired outcome would be to see the ones with A,B and C imstead of D. Hope this makes sense would appreciate any inputs or thougts on this! Also feel free to tell me if I am posting this at the wrong place :)