真实世界使用案例
了解各行业的金融机构如何利用nerous.ai检测洗钱、减少误报并保持监管合规。
银行和传统金融
为零售银行、商业银行和财富管理现代化反洗钱合规。
Regional Commercial Bank
Challenge
Legacy rule-based system generating 12,000 false alerts monthly, overwhelming compliance team of 15 analysts. Unable to detect sophisticated layering schemes across business accounts.
Solution
Deployed nerous.ai with Graph Neural Network analysis of account relationships. Integrated with core banking system via API for real-time wire transfer monitoring. Custom models trained on 5 years of historical transaction data.
Results
Private Wealth Management
Challenge
High-net-worth clients with complex international transactions triggering excessive alerts. Manual review taking 4-6 hours per case. Risk of losing clients due to friction.
Solution
Implemented behavioral profiling for each UHNW client using LSTM models. Created personalized risk baselines considering typical transaction patterns (art purchases, real estate, investments). Real-time risk scoring with context-aware alerts.
Results
加密货币和数字资产
为加密平台提供先进的区块链分析和链上交易监控。
Cryptocurrency Exchange
Challenge
Processing 500K+ daily transactions across multiple blockchains. Traditional chain analysis tools missing cross-chain patterns. Mixer and tumbler transactions requiring manual investigation. Regulatory pressure to identify high-risk counterparties.
Solution
Deployed multi-chain transaction graph analysis. Machine learning models trained on known mixer patterns and sanctioned addresses. Automated wallet clustering and entity resolution across Bitcoin, Ethereum, and major L2s. Integration with Chainalysis and Elliptic for enhanced coverage.
Results
DeFi Platform
Challenge
Complex DeFi interactions creating false positives. Flash loan attacks and wash trading manipulation. Smart contract interactions difficult to classify as legitimate or suspicious. Need to monitor liquidity pools, yield farming, and cross-protocol transactions.
Solution
Built custom models for DeFi-specific patterns including flash loans, MEV, and protocol interactions. Smart contract code analysis to understand transaction intent. Behavioral analysis of wallet patterns across protocols. Real-time monitoring of liquidity pool manipulations and sandwich attacks.
Results
支付处理商和PSP
为支付网关和商户服务提供商提供高速交易监控。
Global Payment Processor
Challenge
Processing 10M+ transactions daily across 45 countries. Money mule networks moving funds rapidly through legitimate accounts. High-velocity merchant fraud. Real-time scoring needed without adding latency to payment flow.
Solution
Deployed real-time risk scoring with <50ms latency. Network graph analysis identifying mule account clusters. Merchant risk profiling using historical patterns and velocity checks. Integration with card networks for fraud intelligence sharing.
Results
Peer-to-Peer Payment App
Challenge
Fraud rings using social engineering to recruit money mules. Legitimate friends/family payments difficult to distinguish from mule activity. Account takeover leading to unauthorized transfers. Balancing fraud prevention with user experience.
Solution
Social network analysis identifying suspicious relationship patterns. Behavioral models detecting account takeover through device, location, and usage pattern changes. Progressive friction (step-up authentication) based on risk scores. Real-time collaboration with other P2P platforms via consortium.
Results
金融科技和数字银行
为数字优先的金融服务和贷款平台提供嵌入式反洗钱。
Digital-Only Neobank
Challenge
Startup bank needing full AML/CFT compliance before launch. Limited budget and team (no compliance staff initially). Need to scale from 0 to 100K customers in first year. Regulatory scrutiny on new digital banks.
Solution
Implemented nerous.ai as complete AML solution - transaction monitoring, customer screening, suspicious activity detection, and SAR filing workflows. Configured pre-built rules for common typologies plus ML models. Automated customer risk rating and ongoing due diligence. Cloud-based deployment with API integration.
Results
Buy Now, Pay Later Provider
Challenge
Return fraud and merchant collusion schemes. Synthetic identity fraud using stolen PII. Rapid growth outpacing manual review capacity. Merchant onboarding risk (fraudulent sellers). CFPB increasing scrutiny of BNPL sector.
Solution
Multi-layered approach: identity verification at signup using document verification and biometrics, transaction monitoring for return patterns and merchant collusion, merchant risk scoring based on return rates and customer complaints, network analysis identifying fraud rings across platforms.
Results
我们如何实现这些结果
我们的技术方法结合多种AI技术,实现全面的反洗钱覆盖。
图神经网络
分析整个交易网络的关系,检测线性系统遗漏的分层、结构化和骡子账户网络。
行为分析
学习每个实体的正常模式并检测偏差。在识别新型洗钱技术的同时减少误报。
实时处理
亚100毫秒交易分析实现即时风险决策。分布式架构可扩展至每天数百万笔交易。