Interpretation of Logistic Regression output in Credit Scoring Currently I'm working on a project involving loan data where I am trying to build a model that will predict a probability of an individual defaulting on a loan. I have binary dependent variable where defaulted loans are denoted with 1. I decided to use logistic regression for this task, but However, I have difficulty interpreting the results of the regression and would greatly appreciate some clarification.
Namely, I would like to know, whether the probabilities calculated by logistic regression should be interpreted as a probability of default? For example, if for some observation, the predicted value is 0.8, should I assume that according to the model, this individual has 80% chance of default?

Yes, as long as the input for the outcome is failure (0,1) where 0-No 1-Yes (default), then the predicted probability from your logistic regression model will be the probability of defaulting.  For your use case of 0.8 or 80% that person does have a default prediction of 80%, so probably not approved.  I would think a cutoff of 50% (0.5) is used, but this would depend on the model, the credit agency, and the input predictors.
Please do consider that a lot of times in finance several models could be used.  It may work out that a model for predicting default with outcome coded 0-no default, 1-default will sometimes employ features which are different from optimal features used for a model for predicting no default with outcome coded 0-default, 1-no default.
Reason I am raising this is because a lot of people try to front-load everything into one model, when in fact if you "divide and conquer" to break up a complex problem into multiple smaller problems your prediction may be better.
In fact, I would never approach a bank or credit agency with results of one model!
