Context-Aware Fintech User Feedback Classification & Urgency Detection

Overview

Fintech companies receive thousands of user feedback entries daily, many of which contain critical operational issues such as failed transactions, account access problems, fraud, or compliance-related complaints.

This project focuses on moving beyond sentiment analysis to build a decision-driven feedback classification system that helps fintech teams:

• Understand what issue a user is facing
• Determine how urgent the issue is
• Prioritize responses based on operational risk rather than emotion

The system is designed with real-world fintech use cases in mind (e.g. Revolut, Wise, Monzo).

Problem Statement

Traditional sentiment analysis labels feedback as positive or negative, but in fintech, this is often misleading. A review can be polite or appreciative while still describing a high-risk issue such as fraud, blocked funds, or account compromise.

Example: “I love the app, but my account was hacked and I can’t access my money.”

• Sentiment: Positive
• Operational risk: Critical

This project reframes user feedback analysis to focus on issue understanding and urgency detection, enabling better triage and escalation decisions.

Objectives

The system aims to:

• Classify user feedback into issue categories
  • Transaction issues
  • Account access & security
  • Refunds & reversals
  • KYC
  • App performance
  • Customer support experience
  • Financial products
  • Product feedback
  • General inquiries
• Detect urgency
  • Identify feedback that requires immediate attention
  • Reduce the risk of delayed responses to critical issues
• Support operational decision-making
  • Prioritization
  • Routing to the right teams
  • Risk-aware triage

Dataset & Annotation Process

The dataset was manually constructed to reflect real-world fintech user feedback, with labeling driven by operational risk rather than sentiment.

Data Collection

• User reviews were manually collected from:
  • Google Play Store
  • Apple App Store
• Reviews were copied sequentially and curated to ensure:
  • Relevance to fintech use cases
  • Coverage across multiple issue types and multiple star ratings
  • Realistic user language and phrasing

Data Preparation

• Raw reviews were cleaned and normalized
• Duplicates and low-signal entries were removed
• Text preprocessing included lowercasing and basic normalization

Labeling & Guidelines

Each feedback entry was annotated with:

• Issue Category
• Urgency Level
• clean_feedback_text word count

A primary labeling guideline was created before annotation, inspired by:

• Fintech industry complaint taxonomies
• Operational support workflows
• Prior hands-on experience in data annotation and evaluation

Labels were assigned using a human-in-the-loop process, prioritizing:

• Operational risk
• User intent
• Business impact over emotional tone

Exploratory Data Analysis (EDA)

Initial exploratory analysis was conducted in Excel to:

• Inspect class distributions
• Identify label imbalance
• Validate urgency proportions
• Spot annotation inconsistencies early

These insights informed modeling decisions such as:

• Stratified train–test splitting
• Macro-averaged evaluation metrics
• The decision to model urgency separately

Modeling Approach

Issue Classification

• Model: Logistic Regression (multiclass)
• Features: TF-IDF (unigrams)
• Reasoning:
  • Interpretable
  • Strong baseline for text classification
  • Fast to train and deploy

Urgency Detection

• Model: Logistic Regression (binary)
• Classes: High urgency vs Not-High urgency
• Evaluation focus: Recall on high-urgency cases (Missing urgent issues is more costly than false alarms)

Evaluation Results

Urgency Classification Performance

• Accuracy: 70%
• High Urgency Recall: 57.7%

The urgency model was evaluated with an emphasis on recall for high-risk cases, reflecting real-world fintech triage priorities. While overall accuracy is reasonable, the key metric is high-urgency recall, which highlights the model’s ability to surface critical issues for escalation. Misclassification analysis was performed to identify:

• Ambiguous language
• Polite phrasing masking serious issues
• Contextual gaps (to be addressed in future work)

Key Insights

• Sentiment alone is insufficient for fintech feedback analysis
• Urgency and issue type provide more actionable signals
• Classical NLP models can deliver meaningful value with proper framing
• Error analysis is essential for understanding real-world failure modes

Limitations

• Small dataset limits generalization
• Bag-of-words TF-IDF does not fully capture context
• Class imbalance affects high-urgency recall

Future Work (Version 2)

• Contextual embeddings (BERT / sentence transformers)
• Joint learning of issue category and urgency
• Cost-sensitive learning to further improve high-urgency recall
• Deployment-oriented pipeline (API / dashboard integration)

Why This Matters

In fintech operations, delayed handling of high-risk issues can lead to:

• Financial loss
• Regulatory exposure
• User churn

This project demonstrates how NLP can support operational triage, not just sentiment reporting.

Tech Stack

• Python
• scikit-learn
• pandas
• numpy
• Jupyter / VS Code
• Excel / Googlesheets