I'm attempting to apply a machine learning regression solution using NN to the following problem:
I have the history of loans granted by a bank, and I need to forecast what loans will be opened in the future based on macroeconomic variables. Amount, Zipcode and Segment are loan features and Unemployment and GDP are macroeconomic features.
Loan Date Amount Zipcode Segment Unemployment GDP Probability 1 Jan-2020 100,000 40921 Corporate 5.1% 2.5% 1.0 2 Oct-2020 250,000 54323 Business 4.9% 3.2% 1.0 3 Mar-2021 223,000 40921 Business 4.8% 3.1% 1.0 4 Dec-2021 562,000 43241 Coporate 5.0% 2.9% 1.0 5 Feb-2022 300,000 54325 Corporate 5.2% 2.7% 1.0
I added Probability = 1.0 to every line because the loan was actually granted. Probability is the label in the machine learning model, and it's what I need to predict with the regressor. If probability is greater than 0.5 then the loan is granted, else is not granted.
Now, I need to predict loans that will be opened next month, Apr-2022 at the moment of this writing. The question is what do I take as input in the prediction. My idea is to take all the loans that were granted in the past, combine with the Unemployment and GDP forecasted for Apr-2022, and see if the Probability is greater than 0.5.
I have two concerns. First, I'd like to know if this model makes sense. Second, since I don't have much control over the number of loans that were granted in the past, if the history is large then the predicted number will also be large, and this is not right.
Any suggestions how to approach this?