Can we have better understanding for bank customers through social media? Social media has faced exponential growth during the last few years, creating a very rich source of information and insights on every aspect more than ever. The customers of mainstream lenders (i.e., banks, credit unions), after all, are normal people who interact on social networks and have connections, opinions, sentiments, and reactions in different contexts. Banking credit score calculations consider various financial parameters, overlooking other personal factors of a bank customer that might have a great impact on his financial abilities. Analyzing social media interactions of a bank customer would give an additional layer of eligibility evaluation to the bank customer, which should influence the overall credit scoring calculations process. In this paper, we propose the Social-based Credit Scoring (Socio-CS) approach as a new method for credit score calculation using machine learning techniques, in which new parameters derived from the social media data are integrated into the financial criteria to provide a global representation of the potential borrower and thus improve the trust level of credit scores. A bank dataset is integrated to a twitter dataset to build a case study for experimentation, considering sentiment analysis. The results show that Random Forest achieves the highest average accuracy of 81%, whereas Logistic Regression and Support Vector Machine achieve 72% and 59%, respectively.