Overview / Usage

Each week, the Consumer Financial Protection Bureau (CFPB) receives thousands of consumer complaints about financial products and services. These complaints must be forwarded to the responsible company and posted on the site after 15 days or when the company responds to the complaint, whichever comes first. Published complaints and solutions help consumers solve their problems and also serve as a repository of help for other consumers to avoid or solve problems on their own. Every complaint provides information about the problems people are having, helping them to identify inappropriate practices and allowing them to stop before they become major problems. The result: better results for consumers and a better financial market for everyone.

Methodology / Approach

Each of the complaints contains information on submission date, company to send the complaint, complaint narrative, among others. However, complaints do not have information on the department to which it should be forwarded. Therefore, in this work three models are used to analyze each complaint: (i) convolutional neural network to classify the narratives; (ii) PCA and (iii) t-SNE to cluster narratives. Pre-trained word2Vec and Glove vectors will be used and compared.

Technologies Used

intel dev cloud and deep learning libraries