# Predictive Analysis of CFPB Consumer Complaints Using Machine Learning

arXiv: https://arxiv.org/abs/2407.06399 · HTML: https://mlsanity.com/paper/2407.06399/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2024.

## Built on by (most cited first, 1 of 1)

- [From Complaint Narratives to Monetary Relief: A Hybrid Machine Learning Framework for CFPB Consumer Complaints](https://mlsanity.com/paper/2606.22664/) (2026)

## Abstract (arXiv)

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.