This paper focus on exploring and analyzing Consumer Finance Complaints data, to find how many similar complaints are there in relation to the same bank or service or product. These datasets fall under the complaints of Credit reporting, Mortgage, Debt Collection, Consumer Loan and Banking Accounting. By using data mining techniques, cluster analysis as well as predictive modeling is applied to obtain valuable information about complaints in certain regions of the Country. The banks that are receiving customer complaints filed against them will analyse the complaint data to provide results on where the most complaints are being filed, what products/ services are producing the most complaints and other useful data. Our model will assist banks in identifying the location and types of errors for resolution, leading to increased customer satisfaction to drive revenue and profitability. © 2018 IEEE.