Latent Dirichlet Allocation (LDA) for Topic Modeling of the CFPB Consumer Complaints
This incremental work aims to improve consumer protection in financial markets by enhancing the efficiency of investigating complaints for the CFPB and related stakeholders.
The authors applied latent Dirichlet allocation (LDA) to analyze CFPB consumer complaint narratives, extracting latent topics and their trends over time to evaluate regulatory effectiveness and support automated detection of emerging issues.
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.