Predictive Analysis of CFPB Consumer Complaints Using Machine Learning

TL;DR

This paper presents a machine learning platform analyzing CFPB complaints to predict response times and types, uncovering themes and trends to aid consumers and regulators in understanding financial service issues.

Contribution

The paper introduces a novel platform combining predictive modeling and topic analysis to enhance understanding of consumer complaints in financial services.

Findings

Accurately predicts complaint response times and types

Identifies common themes and underlying issues in complaints

Provides actionable insights for consumers and regulators

Abstract

This paper introduces the Consumer Feedback Insight & Prediction Platform, a system leveraging machine learning to analyze the extensive Consumer Financial Protection Bureau (CFPB) Complaint Database, a publicly available resource exceeding 4.9 GB in size. This rich dataset offers valuable insights into consumer experiences with financial products and services. The platform itself utilizes machine learning models to predict two key aspects of complaint resolution: the timeliness of company responses and the nature of those responses (e.g., closed, closed with relief etc.). Furthermore, the platform employs Latent Dirichlet Allocation (LDA) to delve deeper, uncovering common themes within complaints and revealing underlying trends and consumer issues. This comprehensive approach empowers both consumers and regulators. Consumers gain valuable insights into potential response wait times, while regulators can utilize the platform's findings to identify areas where companies may require further scrutiny regarding their complaint resolution practices.