ML Chargeback Classification Platform

The patent replaces manual document evaluation and human decision-making with an automated machine learning system that processes chargeback data, evaluates fraud risk, and generates decisions. The system uses trained models to analyze transaction patterns, merchant information, and chargeback characteristics, substituting mechanical human processes with automated computational processes that operate faster and at lower cost while maintaining or improving detection accuracy.

The system enables self-service through automated chargeback evaluation where the machine learning model independently processes chargeback cases without requiring human intervention for each decision. The model uses historical data and patterns to autonomously determine fraud risk classifications, allowing the system to serve itself in making routine chargeback decisions while human operators only intervene in complex or borderline cases.

The patent segments chargeback processing into different risk levels and handles them through different pathways. The machine learning model classifies chargebacks into fraud risk categories (e.g., low risk, medium risk, high risk), allowing routine low-risk cases to be processed automatically at high volume while flagging complex high-risk cases for human review. This segmentation enables the system to achieve high overall productivity while maintaining flexibility for complex cases.

The system applies partial automation where the machine learning model handles the majority of chargeback evaluations automatically, while human operators provide supplementary review only when needed. The model performs excessive analysis by evaluating all chargebacks through its framework, then selectively escalates only those requiring human judgment, achieving high productivity for standard cases while preserving adaptability for exceptions.