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A consumer’s complaints present bank or reporting agency with an opportunity to identify and rectify specific problems with their current product or service. The banks that are receiving customer complaints filed against them will analyze 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. This project assists banks in identifying the location and types of errors for resolution, leading to increased customer satisfaction to drive revenue and profitability. This project finds a correlation between complaints, companies and consumers to refine company applications to better accommodate consumer needs using k-means clustering. In addition, using SVM classification, the complaints sentiment values are analyzed and classified into positive or negative reviews. The project is designed using R Studio 1.0 as front end. The coding language used is R version 3.4.4.