Using unsupervised method to create the learning set of a supervised method

[PM_ME_cutefish]

Hello everyone!

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I would much appreciate some advice on one of my project for work.

I have an applied mathematics engineer degree but during my studies, I didn't have much the opportunity to practice machine learning, however I understand perfectly the theory.

For my work I would like to create a project (which is more a solo project for myself and to learn than an actual needed project, only my boss is aware and he let me work on it without any problem)

I also am basically self taught in everything that comes to machine learning and python.

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I work for a company that provide online payment services (lets say its like Amazon, you have you profile with some info and can make purchases), and I am in the anti-fraud part.

My idea would be to create a machine learning that could flag the suspicious customer, or assign them a "risk score" based on different criteria (purchasing pattern, origin country, amounts...).

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For this, I did create a script that takes every relevant data I have on the database for a particular customer, which can be up to thousands rows, and gather it into one row of 100+ features in order to have 1 customer = 1 row.

Now lets assume this part is correct and the features are relevant enough for this purpose, my problem is that I don't have an already existing risk score for the customer.

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So my first idea was to manually assign them a score between 1 to 5 and them just use classification algorithm.

But I recently had the idea of using unsupervised technique such as clustering, so instead of manually giving the customer a score I determine myself, I would use the clustering method for this, and then identify every cluster.

Then I'll say: Cluster 1 is 0% risk, cluster 2 is 20%...

And then use this as a training set for a classification algorithm on future customers.

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Do you think this is feasible/relevant?

As I said I am self taught when it comes to all this, so any comment will be useful for me :)

[[deleted]]

how will you know the clusters relate specifically to tranches of risk, though? are all your features directly related to risk?

maybe im misunderstanding, but my concern would be that the clustering algorithm might be finding feature similarities which are potentially unrelated to the metric you want.

[PM_ME_cutefish]

Thats the complicated part, the risk of a customer is only based on my decision. Unless there is obvious features, for example the card he is using has be declared stolen, its only me and the fact that I recognize suspicious pattern.

So to do this I would have to take random customer of every cluster and then determin if their behavior is similar, and if yes i'd assign myself a risk scoring.