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

2026-06-21

AI Summary

This study addresses the prediction of whether consumer financial complaints result in monetary relief, aiming to uncover service deficiencies and systemic operational risks within financial institutions. To this end, we propose a hybrid machine learning framework that integrates multi-source heterogeneous features—including complaint narratives, LDA-derived topic representations, engineered textual attributes, and structured firm-level characteristics—and employs an XGBoost classifier with a time-series-aware train-test split to mitigate class imbalance. Our approach is the first to jointly leverage semantic text features and institutional attributes for monetary relief prediction, achieving an AUC-ROC of 0.78 on the test set—a significant improvement over the baseline of 0.69. The model effectively reveals systematic disparities across institutions in complaint resolution outcomes, offering interpretable and actionable insights for early-stage risk monitoring.

Abstract

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.

Problem

• consumer complaints
• monetary relief
• financial institutions
• predictive modeling
• class imbalance

Innovation

• hybrid machine learning
• consumer complaint analysis
• monetary relief prediction
• imbalanced classification
• interpretable text features