Identify the parameter causing the anomaly in a multivariate dataset I have a payment transaction dataset with a large number of predictor variables. I am trying to build a model for anomaly detection and I have evaluated various algorithms/approaches for the same like Isolation Forest, kNN, Autoencoders, and One-class SVM.

I am able to identify if a payment record is an anomaly or not but I am not able to pin-point the predictor variable that is causing the anomaly.

e.g.:

Account  ||  Currency  ||  Beneficiary  ||  Amount ||  isAnomaly(target)

I want to identify if, for an anomalous record, Currency variable is causing the anomaly or Amount variable is causing the anomaly. 

I have gone through the below sources amongst many others but couldn't find anything helpful.

Anomaly Detection in Database

Anomaly Detection in multiple parameters

I have recently started my journey in data science and would be glad if someone could help me with this issue.
Usually it will not be a single feature value that is responsible for the decision of a ML model. Neural Networks, Random Forests, SVM, etc. intend to transform the input in a more beneficial feature space, where making decisions is more easier for them.

As a draw back, this makes interpratability for humans more intricate. Explainability of ML methods is a whole research field.

You could check out some explainability approaches. For autoencoders for example you could use Layerwise Relevancy Propagation (LRP). https://arxiv.org/pdf/1708.08296.pdf