Predictive Analysis of CFPB Consumer Complaints Using Machine Learning

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Bibliographic Details
Main Authors:	Vaishnav, Dhwani, Neethinayagam, Manimozhi, Khaire, Akanksha, Woo, Jongwook
Format:	Preprint
Published:	2024
Subjects:	Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
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author	Vaishnav, Dhwani Neethinayagam, Manimozhi Khaire, Akanksha Woo, Jongwook
author_facet	Vaishnav, Dhwani Neethinayagam, Manimozhi Khaire, Akanksha Woo, Jongwook
contents	This paper introduces the Consumer Feedback Insight & Prediction Platform, a system leveraging machine learning to analyze the extensive Consumer Financial Protection Bureau (CFPB) Complaint Database, a publicly available resource exceeding 4.9 GB in size. This rich dataset offers valuable insights into consumer experiences with financial products and services. The platform itself utilizes machine learning models to predict two key aspects of complaint resolution: the timeliness of company responses and the nature of those responses (e.g., closed, closed with relief etc.). Furthermore, the platform employs Latent Dirichlet Allocation (LDA) to delve deeper, uncovering common themes within complaints and revealing underlying trends and consumer issues. This comprehensive approach empowers both consumers and regulators. Consumers gain valuable insights into potential response wait times, while regulators can utilize the platform's findings to identify areas where companies may require further scrutiny regarding their complaint resolution practices.
format	Preprint
id	arxiv_https___arxiv_org_abs_2407_06399
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Predictive Analysis of CFPB Consumer Complaints Using Machine Learning Vaishnav, Dhwani Neethinayagam, Manimozhi Khaire, Akanksha Woo, Jongwook Distributed, Parallel, and Cluster Computing This paper introduces the Consumer Feedback Insight & Prediction Platform, a system leveraging machine learning to analyze the extensive Consumer Financial Protection Bureau (CFPB) Complaint Database, a publicly available resource exceeding 4.9 GB in size. This rich dataset offers valuable insights into consumer experiences with financial products and services. The platform itself utilizes machine learning models to predict two key aspects of complaint resolution: the timeliness of company responses and the nature of those responses (e.g., closed, closed with relief etc.). Furthermore, the platform employs Latent Dirichlet Allocation (LDA) to delve deeper, uncovering common themes within complaints and revealing underlying trends and consumer issues. This comprehensive approach empowers both consumers and regulators. Consumers gain valuable insights into potential response wait times, while regulators can utilize the platform's findings to identify areas where companies may require further scrutiny regarding their complaint resolution practices.
title	Predictive Analysis of CFPB Consumer Complaints Using Machine Learning
topic	Distributed, Parallel, and Cluster Computing
url	https://arxiv.org/abs/2407.06399

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