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BleepingComputer00 bình luận6 phút đọc
The Four Elevations of Effective Fraud Prevention
Fraud prevention requires visibility across four levels: transaction, account, platform, and network. Monitoring only individual transactions leads to siloed decisions and missed fraud patterns. Account-level tracking reveals behavioral anomalies like new devices or contact changes. Platform-level analysis exposes fraud rings through shared signals like IP, device, and geolocation. Network-level partnerships extend detection by sharing threat intelligence across organizations. A worked banking fraud example illustrates how each elevation adds detection confidence and reduces time-to-action against account takeovers and fund transfers.
Nguồn: https://www.bleepingcomputer.com/news/security/the-four-elevations-of-effective-fraud-prevention. 8sync News chỉ tóm tắt và dẫn link; bản quyền nội dung thuộc tác giả và nguồn gốc.
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Towards Data Science017 phút
The Hot Path Belongs to GBDTs, Agents Own the Cold Path: A Payment-Fraud Benchmark
A reproducible benchmark comparing gradient-boosted decision trees (GBDTs) vs. LLM-based scoring for payment fraud detection across three dimensions: latency, cost, and determinism. On a single CPU core, GBDTs hit p99 latency of 0.15ms vs. ~1,200ms for LLMs — well outside the 100ms ISO 8583 authorization budget. Cost-wise, GBDTs run ~$54/hour at 50K TPS vs. $16,200–$351,000 for LLM tiers. Determinism is the most critical issue for regulated environments: GBDTs return identical scores on identical inputs while LLMs produce hundreds of distinct outputs even at temperature=0. The recommended architecture keeps deterministic tree ensembles on the synchronous hot path and deploys LLM agents on the asynchronous cold path for SAR drafting, evidence gathering, and agent-as-a-judge validation before human review. All benchmark code is open-source and reproducible on a laptop.