Logistic regression: How to improve the pseudo-$R^2?$ Firstly, I would like to ask you opinion about my model: I have a logistic regression model where the dependent variable is late payments (1=late/0=not late) and diesel prices and interest rates as the independent variables. I have checked for the VIF factor which less than 2 for each feature. These give p-values 0.000.
Secondly, would anyone know how could I improve the model? The pseudo $R^2$ value is just $0.02511.$ My goal is to find explanatory variables that could explain the late payments, and I tried several others, but they resulted with high VIF scores.

VIF should play no direct role.  What are the two frequencies of the outcome variable?  Why late vs not late as opposed to number of days late (with right censoring)?  You might do some variable clustering (unsupervised learning) to group some collinear variables but don't exclude variables because of collinearity.
See my notes for some useful information about pseudo $R^2$ and adjusted versions of same: https://hbiostat.org/rmsc/lrm.html#quantifying-predictive-ability and the link contained there.
If you have done due diligence in using subject matter thinking to specify a model, and possibly done some unsupervised learning to live within the confines of your effective sample size, a low $R^2$ may be telling you that it's just hard to predict that outcome.
