[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-11029":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":9,"language":10,"languages":9,"totalLinesOfCode":9,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":15,"stars7d":15,"stars30d":16,"stars90d":15,"forks30d":15,"starsTrendScore":15,"compositeScore":17,"rankGlobal":9,"rankLanguage":9,"license":9,"archived":18,"fork":18,"defaultBranch":19,"hasWiki":20,"hasPages":18,"topics":21,"createdAt":9,"pushedAt":9,"updatedAt":29,"readmeContent":30,"aiSummary":31,"trendingCount":15,"starSnapshotCount":15,"syncStatus":32,"lastSyncTime":33,"discoverSource":34},11029,"EvoAML","RongLiu-AML\u002FEvoAML","RongLiu-AML","Develop an EvoAML framework integrating graph networks and temporal evolution analysis, aimed at addressing cross-industry tracking gaps and translating these methods into BSA\u002FAMLA 2020 compliant solutions.",null,"Python",417,39,20,1,0,318,51.81,false,"main",true,[22,23,24,25,26,27,28],"amla-2020","anti-money-laundering","bsa","fincen","graph-neural-networks","regtech","time-series-analysis","2026-06-12 04:00:53","# EvoAML: Evolutionary Anti-Money Laundering RegTech Framework\n\n[English](README.md) | [中文](README_zh.md)\n\n## 🎯 Proposed Endeavor\nTo develop and advance a smart AML detection framework (EvoAML) integrating graph network cross-industry monitoring and temporal behavior evolution analysis, aimed at bridging the systemic technological gap in current anti-money laundering infrastructure regarding cross-industry fund flow tracking and dynamic money laundering pattern recognition, and to translate this methodology into a deployable solution compliant with U.S. regulatory frameworks (BSA\u002FAMLA 2020).\n\n## 💡 Core Architecture\nEvoAML bridges the gap between state-of-the-art AI research and practical, compliant RegTech solutions.\n1. **Graph-Driven Cross-Industry Tracking**: Employs Graph Neural Networks (GNN) to trace obfuscated money trails across complex supply chains and energy mobility sectors.\n2. **Temporal Evolution Analysis**: Utilizes Dynamic Self-Attention Networks to predict and identify mutating money-laundering behaviors over time.\n3. **AMLA 2020 Compliance Engine**: Automatically translates anomalous AI detections into standardized Bank Secrecy Act (BSA) narrative templates for Suspicious Activity Reports (SARs).\n\n## 📅 Project Roadmap (6-Phase Plan)\nFor detailed technical breakdowns, see [development_phases.md](docs\u002Fplanning\u002Fdevelopment_phases.md).\n\n- **Phase 1:** Foundation & Compliance Architecture Setup ✅ (Current)\n- **Phase 2:** Heterogeneous Data Ingestion & Preprocessing\n- **Phase 3:** Graph-Driven Tracking Integration (GNN Module)\n- **Phase 4:** Temporal Behavior Evolution Analysis\n- **Phase 5:** BSA\u002FAMLA 2020 Compliance Engine & SAR Generation\n- **Phase 6:** System Simulation, Visualization Dashboard & v1.0 Release\n\n## 🚀 Quick Start\nSee the `examples\u002F` directory for demonstrations of cross-industry tracking and automated SAR generation workflows.\n","EvoAML是一个集成图网络和时间演化分析的反洗钱监管技术框架，旨在解决跨行业资金流动追踪问题，并符合BSA\u002FAMLA 2020法规要求。该项目利用图神经网络（GNN）进行跨行业的复杂资金流向追踪，同时采用动态自注意力网络来预测和识别随时间变化的洗钱行为模式。此外，EvoAML还具备自动将异常检测结果转化为标准化可疑活动报告（SARs）的功能，确保合规性。此框架适用于需要加强反洗钱监控能力的金融机构以及涉及复杂供应链的企业，特别是在面对日益复杂的洗钱手段时提供强有力的技术支持。",2,"2026-06-11 03:31:05","CREATED_QUERY"]