Latent Dirichlet Allocation (LDA) for Topic Modeling of the CFPB Consumer Complaints

TL;DR

This paper applies latent Dirichlet allocation (LDA) to analyze CFPB consumer complaints, extracting topics and trends to evaluate regulatory impacts and support consumer protection efforts.

Contribution

It introduces an LDA-based method to identify and track latent topics in complaint narratives, aiding regulatory analysis and decision support.

Findings

Identified key complaint topics and their trends over time.

Demonstrated potential for automating complaint analysis.

Supported regulatory evaluation and consumer protection improvements.

Abstract

A text mining approach is proposed based on latent Dirichlet allocation (LDA) to analyze the Consumer Financial Protection Bureau (CFPB) consumer complaints. The proposed approach aims to extract latent topics in the CFPB complaint narratives, and explores their associated trends over time. The time trends will then be used to evaluate the effectiveness of the CFPB regulations and expectations on financial institutions in creating a consumer oriented culture that treats consumers fairly and prioritizes consumer protection in their decision making processes. The proposed approach can be easily operationalized as a decision support system to automate detection of emerging topics in consumer complaints. Hence, the technology-human partnership between the proposed approach and the CFPB team could certainly improve consumer protections from unfair, deceptive or abusive practices in the financial markets by providing more efficient and effective investigations of consumer complaint narratives.