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Financial Fraud Training Container

Financial Fraud Training Container - Home Financial Fraud Training Container - Home

Financial Fraud Training Container

Table of Contents

  • Overview
    • Financial Fraud Detection — Conceptual Overview
  • Getting Started
    • Quick Start — End-to-End in 5 Steps
    • Prerequisites
    • Support Matrix
    • Run Training Using Financial Fraud Training
  • Data Organization
    • Data Layout — Rigorous Reference
    • Preparing Your Data
  • Training
    • Training
    • Multi-GPU Training
    • Advanced Training Options
    • Automated Hyperparameter Optimisation — LLM Tuning + Grid Search
  • Deployment
    • Generated Artifact Layout
    • Serving the Trained Model
  • Inference
    • Sending Inference Requests
  • Supplementary
    • Testing, Performance, and Troubleshooting
  • Appendices
    • Appendix A — Complete Config Schema Reference
    • Appendix B — Glossary
    • Appendix C — Default LLM Tuning Prompt
  • Release Notes
    • Versions
  • Inference
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Inference#

  • Sending Inference Requests
    • 7.1 Standard Fraud Scoring — CLI Client
    • 7.1b Node Prediction Client (GNN_XGBoost_NP)
    • 7.2 LLM Explainability Mode
    • 7.3 Micro-Batch Gateway Simulation
    • 7.4 Programmatic Python Client
    • 7.5 REST API Using curl
    • 7.6 Interpreting Results and Setting Thresholds

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Serving the Trained Model

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Sending Inference Requests

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Last updated on Aug 20, 2026.