Negative Review or Complaint? Exploring Interpretability in Financial Complaints

Abstract

In the financial service sector, customer service is the most critical tool for long-term business growth. A financial complaint detection (CD) system could aid in the identification of shortcomings in product features and service delivery. This could further ensure faster resolution of customer complaints and thereby help retain existing clients and attract new ones. Prior research has prioritized only complaint identification and prediction of the corresponding severity levels; the first aim is to categorize a textual element as a complaint or a noncompliant. The other attempts to classify complaints into several severity levels based on the degree of risk the complainant is willing to endure. Identifying the reason or source of a complaint in a text is a significant but underexplored area in natural language processing study. We propose an explainable complaint cause identification approach with a dyadic attention mechanism at the sentence and word levels, enabling it to give varying amounts of emphasis to more and less important information. As the first subtask, the model simultaneously trains CD, sentiment detection, and emotion recognition tasks. Afterwards, we identify the complaint's cause and its severity level. To do this, the causal span annotations for complaint tweets are added to an existing financial complaints corpus. The findings suggest that conventional computing techniques can be adapted to solve extremely relevant new problems, generating novel opportunities for research ^{1 1}

The code and dataset are available at https://github.com/sarmistha-D/Complaint-HaN

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