The Hot Path Belongs to GBDTs, Agents Own the Cold Path: A Payment-Fraud Benchmark

Short summary

This Towards Data Science article benchmarks Gradient Boosted Decision Trees (GBDTs) against AI agents for payment fraud detection, comparing latency, cost, and reproducibility. The analysis positions GBDTs as superior for real-time processing ('hot path') and agents for batch or complex scenario analysis ('cold path'). The reproducible benchmark helps teams make informed decisions on which approach suits their fraud-detection workload and performance requirements.

• •GBDTs win for real-time fraud detection (low-latency 'hot path')
• •Agents excel at complex/batch analysis ('cold path')
• •Benchmark includes latency, cost, and reproducibility metrics

Generated with AI, which can make mistakes.