Why we prefer VIF if we can find multicollinearity from correlation matrix as well?
The correlation matrix is not a reliable measurement for multicollinearity  because it only considers the pairwise effects. Unfortunately, multicollinearity is defined as:


  Phenomenon in which two or more predictor variables in a multiple regression model are highly correlated,


Do you see the point? You'll need to consider the correlation with all other variables in your data set, not just 1-to-1 pairwise comparison.

VIF addresses the issue.