Date of Award


Document Type


Degree Name

Master of Science in Computer Science


School of Computer Science and Engineering

First Reader/Committee Chair

Dr. Bilal Khan


Within the dynamic and highly competitive financial industry, the timely and efficient resolution of customer complaints stands as a central challenge, particularly in the intricate domain of mortgage services. The traditional processes for handling these complaints have long been recognized as laborious and resource-intensive, a situation that financial institutions, including the esteemed Wells Fargo, are keen to improve.

Currently, the industry largely relies on basic data analytics for identifying trends in customer complaints. However, this approach has its limitations, especially when dealing with complaints within the mortgage services domain. In response to this challenge, this research advocates the adoption of advanced predictive models as a groundbreaking solution. These models, powered by Random Forests hold the promise of transforming the management of mortgage-related complaints fundamentally.

The Random Forests model, known for its capacity to analyze complex, non- linear relationships within data, is poised to revolutionize the prediction of customer complaint resolution outcomes. By analyzing a vast dataset from the Consumer Complaint Database, comprising 3,211,591 complaints spanning a decade, the model aspires whether a mortgage-related complaint will be swiftly resolved or require an extended resolution time.

The anticipated outcomes of this endeavor encompass a transformative impact on the mortgage-related complaint resolution landscape.

While this research is a pivotal step forward, broader complaint categories, and further refined predictive models could enhance the efficacy of complaint management and resolution processes.

Included in

Data Science Commons