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.

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Data visualization is the process of visualizing data through tables, charts, graphs, maps, and other visual aids. Data visualization is often thought of as a descriptive tool, but it is also important for use in predictive and prescriptive analytics. It serves as a tool for descriptive data exploration, but also for communicating insights from data and from analytical models. In this tutorial, we provide a concise discussion of best practices for data visualization for effective communication.

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