ML models for Customer Relationship Analysis in Finance
The purpose of this research project was to analyze customer complaint data from financial institutions and identify areas of opportunity for these institutions to improve their customer satisfaction rate. In addition to pointing out areas for improvement, this paper also looks into similar research and tries to understand if themes found in this analysis are consistent with those done by other researchers. Banking is an essential piece to everyday life for all people across the world. Banks need to ensure that their products and processes are simple and accessible to all. Although banks have a monopoly on our financial needs their desire to retain existing customers and gain new ones drives the necessity of providing excellent and timely customer service. The study was conducted using a dataset of over two million customer complaint records and examining what were the top three financial institutions receiving complaints and which products received them. In addition, other aspects of complaints such as state of origination was also looked at. Analysis was done using machine learning, python, tableau and other tools to show the data points and their correlation. Understanding the top financial institution's methods of handling customer complaints, we are able to make recommendations for further product improvements to increase customer satisfaction. Concluding the research project is a list of challenges and opportunities for further research projects. In addition, there are recommendations for the financial institutions investigated in this project on how to move forward from analyzing customer complaint data.
Finance, Machine learning