# From Complaint Narratives to Monetary Relief: A Hybrid Machine Learning Framework for CFPB Consumer Complaints

arXiv: https://arxiv.org/abs/2606.22664 · HTML: https://mlsanity.com/paper/2606.22664/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 21 Jun 2026.

## Builds on (most cited first, 6 of 6)

- [The Adoption and Efficacy of Large Language Models: Evidence From Consumer Complaints in the Financial Industry](https://mlsanity.com/paper/2311.16466/) (2023)
- [Latent Dirichlet Allocation (LDA) for Topic Modeling of the CFPB Consumer Complaints](https://mlsanity.com/paper/1807.07468/) (2018)
- [Performance of diverse evaluation metrics in NLP-based assessment and text generation of consumer complaints](https://mlsanity.com/paper/2506.21623/) (2025)
- [A Regulatory Governance Framework for AI-Driven Financial Fraud Detection in U.S. Banking: Integrating OCC, SR 11-7, CFPB, and FinCEN Compliance Requirements for Model Development, Validation, and Monitoring Lifecycles](https://mlsanity.com/paper/2605.04076/) (2026)
- [Predictive Analysis of CFPB Consumer Complaints Using Machine Learning](https://mlsanity.com/paper/2407.06399/) (2024)
- [Reliability of Topic Modeling](https://mlsanity.com/paper/2410.23186/) (2024)

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

- [Distributional sentiment modeling and anomaly detection for consumer complaint assessment](https://mlsanity.com/paper/2609.31653/) (2026)

## Abstract (arXiv)

Consumer financial complaints provide a valuable source of information for identifying service failures, dispute frictions, and operational deficiencies in consumer-facing financial institutions. This paper proposes a hybrid machine learning framework for predicting monetary relief outcomes using Consumer Financial Protection Bureau complaint data. We formulate the task as an imbalanced binary classification problem, where complaints closed with monetary relief are treated as compensable outcomes. The proposed framework integrates multiple sources of predictive information, including complaint narrative text, LDA-based topic representations, interpretable text-engineered features, and structured categorical attributes such as company and state. An XGBoost classifier is trained using a temporal train-test split, with earlier complaints used for model development and more recent complaints reserved for out-of-sample evaluation. Compared with a TF-IDF baseline, the proposed framework substantially improves predictive performance, increasing AUC-ROC from 0.69 to 0.78 and improving PR-AUC under class imbalance. Feature importance analysis shows that textual signals, latent complaint topics, and company identity all contribute meaningful predictive information. In particular, company-level effects reveal systematic variation in complaint resolution patterns across financial institutions. These findings suggest that consumer complaint narratives can serve as alternative data for monitoring consumer harm, identifying firm-level operational weaknesses, and supporting early-stage risk surveillance in consumer finance.