why model is giving high accuracy of 84% but very low AUC 13%?
No, your model is not fine. 
In your dataset around 88% records belong to "Label 0", which makes your model bias to "Label 0". Thus, even though your AUC is low, it shows 84% accuracy as most of the data belongs to "Label 0".
You can undersample records belong to "Label 0" or oversample records belong to "Label 1" to make your model more accurate. 
Hope it helps.
I have built model which gives me 84% accuracy for random forest and support vector machine but giving very low auc of 13% only. I am building this in python and I am new to machine learning and data science.
I am predicting 0 and 1 labels on dataset. My overall dataset is having records of 30744.
Label 1 - 6930
Label 0 - 23814
Could you please advise if this is fine? Is model is getting overfitted?
Appreciate any suggestion on improving auc?