  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.
