Latent Dirichlet Allocation (LDA) for Topic Modeling of the CFPB Consumer Complaints (1807.07468v1)

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.

• The paper presents a sophisticated application of Latent Dirichlet Allocation (LDA) for topic modeling over 86,803 Consumer Financial Protection Bureau (CFPB) consumer complaint narratives.
• The methodology involves extensive data preprocessing, applying LDA to a term-document matrix to automatically infer 40 distinct latent topics from the text.
• Results reveal evolving patterns in consumer issues, identifying specific topics and trends using a 'topic popularity' metric to provide insights for regulators and financial institutions.

Latent Dirichlet Allocation for Analyzing CFPB Consumer Complaint Narratives

The paper presents a sophisticated application of Latent Dirichlet Allocation (LDA) for the topic modeling of consumer complaint narratives from the Consumer Financial Protection Bureau (CFPB) dataset. The authors aim to address a significant gap in formal analyses of textual data within the CFPB's publicly available database, noting the substantial volume of unstructured consumer narratives that conventional labeling processes inadequately capture.

Methodology and Data

The manuscript details an extensive data preprocessing workflow that involves typical text mining tasks such as text normalization, tokenization, stop-word removal, and stemming, leading to the construction of a term-document matrix. This matrix then serves as input for the LDA model. Notably, the authors harness LDA's capability to automatically infer topics from the raw text by estimating the latent topic structure through hierarchical Bayesian methods.

The CFPB's database encompasses a voluminous 86,803 consumer complaint narratives after preprocessing. Each complaint's narrative, treated as a document in LDA's framework, is modeled as a mixture of latent topics revealed through probabilistic assignments, contrasting the CFPB's existing labeling convention that relies on predetermined drop-down menu selections.

Results and Analysis

The LDA modeling efforts brought forth 40 distinct topics, each characterized by a set of high-probability words. The derived topics cover a broad spectrum of financial issues, such as "Identity Theft," "Credit Reporting," and "Auto Loan/Dealership," with some topics revealing concerns not encapsulated by the CFPB's conventional issue labels, like "Divorce and Ex-spouse."

Crucially, the authors address the patterns of topic prominence over time, developing a "topic popularity" metric to quantify these dynamics. The results highlight both increasing trends in topics like "Credit Reporting" and decreasing trends in others, such as "Mortgage/Loan Modification and Foreclosure," giving insight into the evolving landscape of consumer financial complaints and the impact of regulatory changes.

Implications and Future Directions

The introduction of LDA into the analysis of CFPB data signifies an advancement in eliciting actionable insights from vast amounts of textual complaint data. By uncovering latent topic structures, regulators and financial institutions can better understand consumer grievances, refining policies and responses to meet evolving demands.

Practically, the generated topic trends serve as a feedback loop for evaluating the efficacy of regulatory interventions over time. For instance, a persisting increase in the popularity of certain topics may indicate areas where regulation inadequately addresses consumer needs.

For future exploration, the integration of LDA with real-time data processing capabilities in a decision support system (DSS) could revolutionize how consumer narratives inform policy and institutional behavior. By continuously monitoring emerging topics, stakeholders could preemptively address issues before they inflate into systemic risks.

Overall, this paper contributes significantly to the corpus of text mining applications in regulatory contexts, leveraging the strengths of LDA to transform unstructured complaint data into structured insights that facilitate superior consumer protection strategies.