A computer implemented method of monitoring a machine learning model for enabling identification of fraudulent activity in a network, and providing corrective actions, the method comprising: applying a machine learning model to a dataset relating to activity in the network, the dataset comprising a plurality of features as inputs into the machine learning model, the machine learning model having been trained on a training dataset to identify fraudulent activity in the network; monitoring a feature of the plurality of features to determine drift of the feature from an expected value indicating incorrect identification of whether the activity is fraudulent, wherein the step of monitoring comprises: determining a feature distribution for the feature at a current time; comparing the determined feature distribution to a reference feature distribution for the feature, the reference feature distribution determined from the training dataset; determining an unexpected discrepancy between the determined feature distribution and the reference feature distribution that indicates feature drift; and taking corrective action based on determining the unexpected discrepancy between the determined feature distribution and the reference feature distribution that indicates the feature drift; wherein the step of monitoring a feature further comprises: tracking a change in the feature distribution for the feature over time through performing the step of: determining a feature distribution for the feature at various points in time and comparing the determined feature distribution at each point in time to the reference feature distribution for the feature to generate a comparison value at each time; and wherein the step of determining an unexpected discrepancy between the determined feature distribution and the reference feature distribution that indicates drift further comprises: comparing an average comparison value, derived from said comparison value at each time, over time to determine change in the feature distribution indicating the feature drift. applying a machine learning model to a dataset relating to activity in the network, the dataset comprising a plurality of features as inputs into the machine learning model, the machine learning model having been trained on a training dataset to identify fraudulent activity in the network; monitoring a feature of the plurality of features to determine drift of the feature from an expected value indicating incorrect identification of whether the activity is fraudulent, wherein the step of monitoring comprises: determining a feature distribution for the feature at a current time; comparing the determined feature distribution to a reference feature distribution for the feature, the reference feature distribution determined from the training dataset; determining an unexpected discrepancy between the determined feature distribution and the reference feature distribution that indicates feature drift; and taking corrective action based on determining the unexpected discrepancy between the determined feature distribution and the reference feature distribution that indicates the feature drift; determining a feature distribution for the feature at a current time; comparing the determined feature distribution to a reference feature distribution for the feature, the reference feature distribution determined from the training dataset; determining an unexpected discrepancy between the determined feature distribution and the reference feature distribution that indicates feature drift; and taking corrective action based on determining the unexpected discrepancy between the determined feature distribution and the reference feature distribution that indicates the feature drift; wherein the step of monitoring a feature further comprises: tracking a change in the feature distribution for the feature over time through performing the step of: determining a feature distribution for the feature at various points in time and comparing the determined feature distribution at each point in time to the reference feature distribution for the feature to generate a comparison value at each time; and wherein the step of determining an unexpected discrepancy between the determined feature distribution and the reference feature distribution that indicates drift further comprises: comparing an average comparison value, derived from said comparison value at each time, over time to determine change in the feature distribution indicating the feature drift. tracking a change in the feature distribution for the feature over time through performing the step of: determining a feature distribution for the feature at various points in time and comparing the determined feature distribution at each point in time to the reference feature distribution for the feature to generate a comparison value at each time; and wherein the step of determining an unexpected discrepancy between the determined feature distribution and the reference feature distribution that indicates drift further comprises: comparing an average comparison value, derived from said comparison value at each time, over time to determine change in the feature distribution indicating the feature drift.