Customer complaints constitute a valuable yet underexploited source of information for understanding consumer harm, operational weaknesses, and emerging systemic risks within the U.S. banking sector. As digital banking services expand and customer interaction channels multiply, the volume and complexity of complaint data have grown rapidly, rendering traditional manual review and rule based monitoring approaches inadequate. Delayed or incomplete analysis of complaints can allow localized service failures or misconduct to escalate into broader financial stability and consumer protection concerns. This study proposes an AI-driven customer complaint analytics framework designed to transform large scale, unstructured complaint narratives into actionable risk intelligence. The framework integrates natural language processing techniques for text preprocessing, automated classification, sentiment analysis, and topic modeling with machine learning based risk scoring mechanisms. By analyzing complaint frequency, severity, sentiment trends, and thematic concentration across products and institutions, the proposed approach enables early detection of consumer harm patterns and potential systemic risk signals. Empirical evaluation using U.S. banking complaint datasets demonstrates that AI-based analytics significantly outperform conventional methods in terms of classification accuracy, detection speed, and scalability. The results highlight the ability of the proposed system to support proactive regulatory supervision, enhance internal bank compliance functions, and improve transparency and consumer trust. Overall, this research shows that AI-driven complaint analytics can serve as an effective early warning and decision support tool, contributing to both systemic risk reduction and strengthened consumer protection in modern banking environments.
Md Imran Hossain Bhuiyan (Tue,) studied this question.