I am building a probability of default model based on behavioral information. The dataset is a loan portfolio, which contains 4 types of loans: mortgage, unsecured loans, car loans and credit cards. The goal is to predict the credit quality of the client after loan issuance based on his behavioral information, such as current account balances, overdue amounts, days past due and various other factors. In order to enhance the model performance I was thinking of splitting the model into several sub-models for different types of clients. For example we could split the dataset into two subsets, clients with and without overdue payments, and then fit separate models for each sub-segment.
Are there any quantitative ways to establish if it would be beneficial to split the model into sub-models? The usual approach is to fit a variety of models and see what works best, however I find this approach to be quite rudimentary and time consuming.
If yes, how could we identify the optimal segments that the data should be split into? In my example I gave a split based the presence of overdue payments, however, another way could be to create separate models based on the products that the client has.
So far I was doing such splits simply based on business logic and data availability, however it would be great to find an algorithmic way to do so as well.
I am limited to only using logistic regression due to regulatory requirements