I want to build a model to support decision making in order to propose or not loan insurance to clients. Because sometimes clients asking loan and loan insurance have less chance to have their loan accepted by a bank and sometimes more chances.

There are three actors in the problem: a bank, a loaner applicant (someone who ask for a loan) and a counselor. The counselor studies the loaner application and if it has a good profile it will propose to him loan from banks that fits his profile. Then the application is sent to the bank but the bank could refuse the applicant (based on criteria we don't know).

The counselor has also to decide whether or not he will propose to the loaner applicant a loan insurance. I want to build a model for that decision.

The risk is that some banks reject loan applicant who accepts a loan insurance and other banks accept more applicants with a loan insurance. But there aren't rules regarding banks since some banks accept or reject applicants with loan insurance according of the profile of the applicants and the type of acquisition.

Thus, the profile of the applicant and the bank he is applying to can matter in their rejection from banks but all criteria influencing the decision are quite uncertain.

Even though it is a classification problem, in my dataset I don't have a good label for loan insurance proposal. I have a features that says if the insurance was proposed or not and to less than 1% the insurance was proposed. I have a label that says if the clients application for a loan was accepted or not.

Thus, the data I have is former applicants profile - and banks who propose loan according to what the applicant wants - and if they were accepted or not by the bank and if they wanted a loan insurance or not.

I thought of combining the label with the information but I don't really know how or maybe doing multi-label classification but also I don't really know if it does fit to the problem.


1 Answer 1


It sounds like the problem can solved with business rules - have an expert write down what choices should be made by whom under what circumstances.

Not all problems can be solved with machine learning.


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