AI-Powered Chargeback Classification System

Project Overview

Developed a sophisticated machine learning system to automate the classification of customer chargebacks at FlightHub, transforming a manual process into an intelligent, evidence-based classification pipeline that processes thousands of disputes with high accuracy and consistency.

 Business Impact & Benefits

  • Operational Efficiency

    • Automated Processing: Eliminated manual review for 60-70% of cases

    • Consistency: Standardized classification criteria across all disputes

    • Speed: Reduced processing time from hours to minutes per case

    • Scalability: Handles thousands of cases with minimal human intervention

    • 25-60% API Cost Reduction through intelligent token compression

    • Reduced Manual Labor: Freed specialist staff for complex cases requiring human judgment

    • Quality-Based Routing: Optimized resource allocation based on evidence strength

    • Multi-tier Confidence Scoring: Confidence levels from 0.6-0.98 based on evidence quality

    • Evidence Hierarchy: Weighted reliability system prioritizing system data over subjective feedback

    • Pattern Learning: Historical case analysis for improved future classifications

    • Review Rate Optimization: 15-30% reduction in unnecessary manual reviews

Advanced Technical Innovations

    • Developed sophisticated prompt management system with version control

    • Multi-layered prompt hierarchy optimized for different evidence types

    • A/B testing framework for prompt optimization

    • Confidence calibration based on evidence quality tiers

    • Incremental sync architecture for updated evidence detection

    • Batch processing with progress tracking and error recovery

    • Comprehensive logging and monitoring for system observability

    • Graceful fallback mechanisms for system reliability

    • Designed normalized staging table architecture

    • Implemented composite indexing for performance optimization

    • Built audit trail system for regulatory compliance

    • Created analytics-ready data structure for business intelligence

Machine Learning & AI

  • Large Language Model (LLM) integration and optimization

  • Context engineering and prompt optimization

  • Multi-class classification with confidence scoring

  • Evidence quality assessment algorithms

Software Engineering

  • Object-oriented Python development with clean architecture

  • Database design and optimization (MySQL)

  • RESTful API development and integration

  • Comprehensive error handling and logging

Skills Demonstrated

Data Engineering

  • ETL pipeline design and implementation

  • Multi-source data integration and normalization

  • Real-time and batch processing architectures

  • Data quality validation and monitoring

System Design

  • Scalable microservices architecture

  • Fault-tolerant processing with graceful degradation

  • Performance monitoring and observability

  • Version control and deployment strategies

Technical Architecture

Core Technologies

  • Python 3.x with advanced ML libraries (pandas, SQLAlchemy)

  • Azure OpenAI integration for large language model processing

  • MySQL for data warehousing and analytics

  • Multi-database integration (Loss Prevention, OTA systems, Chat platforms)

  • RESTful API design for scalable data ingestion

Data Integration & Processing

Multi-Source Evidence Pipeline

Engineered comprehensive data sync system integrating:

  • Schedule Changes & Cancellations (highest reliability system data)

  • Real-time Chat & Call Transcripts (customer interaction records)

  • Customer Feedback & Reviews (direct input analysis)

  • Internal Booking Notes (operational documentation)

  • Ticket Status & System Logs (supplementary indicators)

Advanced AI Features

    • Applied LangChain's four-strategy context engineering framework

    • Write Context: Persistent reasoning trails and decision audit logs

    • Select Context: Smart retrieval of similar historical cases for pattern learning

    • Compress Context: 30-70% token usage reduction while preserving accuracy

    • Isolate Context: Multi-agent routing based on case complexity

    • Developed hierarchical evidence reliability framework

    • Dynamic confidence thresholds based on evidence quality (High/Medium/Low/Insufficient)

    • Content quality assessment beyond simple presence detection

    • Pre-LLM filtering to optimize processing costs

Intelligent Classification Categories

  • Cancelled Flight (Voluntary/Involuntary with sub-categorization)

  • Schedule Change (Customer-requested vs Airline-imposed)

  • Customer Error (Visa issues, missed flights, incorrect data)

  • Agent Error (CS/Sales agent mistakes with verification)

  • Fraud Detection (True fraud vs unauthorized transactions)

Project Outcomes

This system successfully automated a critical business process while maintaining high accuracy standards and providing detailed audit trails for regulatory compliance. The solution demonstrates expertise in AI/ML implementation, data engineering, and enterprise software development, showcasing the ability to deliver production-ready systems that provide significant business value.

The project exemplifies modern AI application development, combining cutting-edge language models with robust engineering practices to solve real-world business challenges at scale.

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Using Text Analysis to Improve Chargeback Management and Customer Experience