Is there a problem with multicollinearity and for splines regression?

When using natural (i.e. restricted) cubic splines, the basis functions created are highly collinear, and when used in a regression seem to produce very high VIF (variance inflation factor) statistics, signaling multicollinearity. When one is considering the case of a model for prediction purposes, is this an issue? It seems like it will always be the case because of the nature of the spline construction.

Here is an example in R:

library(caret)
library(Hmisc)
library(car)
data(GermanCredit)

spl_mat<-rcspline.eval(GermanCredit$Amount, nk=5, inclx=TRUE) #natural cubic splines with 5 knots class<-ifelse(GermanCredit$Class=='Bad',1,0) #binary target variable
dat<-data.frame(cbind(spl_mat,class))

cor(spl_mat)

OUTPUT:
x
x 1.0000000 0.9386463 0.9270723 0.9109491
0.9386463 1.0000000 0.9994380 0.9969515
0.9270723 0.9994380 1.0000000 0.9989905
0.9109491 0.9969515 0.9989905 1.0000000

mod<-glm(class~.,data=dat,family=binomial()) #model

vif(mod) #massively high

OUTPUT:
x         V2         V3         V4
319.573 204655.833 415308.187  45042.675

UPDATE:

I reached out to Dr. Harrell, the author of Hmisc package in R (and others) and he responded that as long as the algorithm converges (e.g. the logistic regression) and the standard errors have not exploded (as Maarten said below) - and the model fits well, best shown on a test set, then there is no issue with this collinearity.

Further, he stated (and this is present on page 65 of his excellent Regression Modeling Strategies book) that collinearity between variables constructed in an algebraic fashion like restricted cubic splines is not an issue as multicollinearity only matters when that collinearity changes from sample to sample.

• You can always orthogonalize the generated splines (for example the rcsgen Stata command uses Gram-Schmidt orthogonalizaton) – boscovich Sep 25 '13 at 21:12