How to generate response variable during machine learning?
No.  You can't.  You need ground truth.  You're asking "if I don't know which claims are fraudulent, can an algorithm somehow determine that for me?"  The answer of course is no: the algorithm doesn't know anything more than you do about insurance fraud -- if anything, it knows even less.

This is one of the challenges with using machine learning.  You typically need a lot of labelled data: data that has been labelled with ground truth (e.g., whether the insurance claim is legit or fraudulent).  Supervised learning is data-driven: given some instances where we know the correct answer, it tries to figure out some rules to use to classify other instances where we don't know the answer.  For this to work, it needs a training set of instances where we do know the correct answer.

Now there are some techniques that can be helpful.  You might be able to use active learning to try to figure out claims to manually label, to try to reduce the number of examples you need to label.  You might be able to use clustering to cluster the instances, and then hand-label a few instances from each cluster.  But ultimately, you'll need some labelled instances to train on and evaluate the accuracy of your method.

See also How to identify labels in unsupervised learning? and Why is automatically labeling data in unsupervised learning hard? and
What exactly is the difference between supervised and unsupervised learning? and
What would be a decent threshold for classification problem?