anomaly detection - conceptual help on machine learning I am working on Anomaly detection model problem for a finance data set -  set of card transactions. My team member suggested an idea that 
" First train the model with normal instances of data and to the trained model pass the anomalous data during testing to see whether the model will be able to detect the anomaly or not."
Is it possible like this with any Machine Learning algorithms. If so, can anybody suggest what algorithms can be used for this idea?

An additional question: How about the situation when there is no properly labelled fraudulent data and we have just transactional information. Does semi-supervised learning works? I do not have experience in this type of ML approach. Can anybody suggest good resources for this and also suggest can Semi-supervised learning or hybrid supervised learning works for anomaly/ fraud detection?
There is two options:

You have a label

Then what you probabibly have is a binary classification problem, probably unbalanced. If this happens follow this https://towardsdatascience.com/practical-tips-for-class-imbalance-in-binary-classification-6ee29bcdb8a7

You have no label

Then you have an unsupervised learning problem, in finance Isolation Forest is really used. If this is the case have a look at this https://towardsdatascience.com/outlier-detection-with-isolation-forest-3d190448d45e