High-performance financial risk analysis plugin with portfolio risk scoring, anomaly detection, market regime classification, and compliance reporting using RuVector WASM packages.
npm install @claude-flow/plugin-financial-risk
A high-performance financial risk analysis plugin combining sparse inference for efficient market signal processing with graph neural networks for transaction network analysis. The plugin enables real-time anomaly detection, portfolio risk scoring, and automated compliance reporting while maintaining the explainability required by financial regulators (SEC, FINRA, Basel III).
- Portfolio Risk Analysis: Calculate VaR, CVaR, Sharpe ratio, Sortino ratio, and max drawdown
- Anomaly Detection: Detect fraud, AML violations, and market manipulation in transactions
- Market Regime Classification: Identify current market regime through historical pattern matching
- Compliance Checking: Automated verification against Basel III, MiFID II, Dodd-Frank, AML, and KYC
- Stress Testing: Run historical and hypothetical stress scenarios on portfolios
``bash`
npm install @claude-flow/plugin-financial-risk
`bash`
npx claude-flow plugins install --name @claude-flow/plugin-financial-risk
`typescript
import { FinancialRiskPlugin } from '@claude-flow/plugin-financial-risk';
// Initialize the plugin
const finance = new FinancialRiskPlugin({
marketDataPath: './data/market',
modelPath: './data/models',
auditLogPath: './logs/audit'
});
// Analyze portfolio risk
const risk = await finance.portfolioRisk({
holdings: [
{ symbol: 'AAPL', quantity: 100, assetClass: 'equity' },
{ symbol: 'GOOGL', quantity: 50, assetClass: 'equity' },
{ symbol: 'TLT', quantity: 200, assetClass: 'bond' }
],
riskMetrics: ['var', 'cvar', 'sharpe'],
confidenceLevel: 0.95,
horizon: '1d'
});
// Detect anomalies in transactions
const anomalies = await finance.anomalyDetect({
transactions: [
{ id: 'tx-001', amount: 50000, timestamp: '2024-01-15T10:30:00Z', parties: ['ACC-123', 'ACC-456'] },
{ id: 'tx-002', amount: 1000000, timestamp: '2024-01-15T10:31:00Z', parties: ['ACC-123', 'ACC-789'] }
],
sensitivity: 0.8,
context: 'fraud'
});
// Check regulatory compliance
const compliance = await finance.complianceCheck({
entity: 'FUND-001',
regulations: ['basel3', 'mifid2'],
scope: 'capital',
asOfDate: '2024-01-15'
});
`
Calculate comprehensive portfolio risk metrics.
`typescript
const result = await mcp.invoke('finance/portfolio-risk', {
holdings: [
{ symbol: 'SPY', quantity: 1000, assetClass: 'equity' },
{ symbol: 'BND', quantity: 500, assetClass: 'bond' },
{ symbol: 'GLD', quantity: 200, assetClass: 'commodity' }
],
riskMetrics: ['var', 'cvar', 'sharpe', 'max_drawdown'],
confidenceLevel: 0.99,
horizon: '1w'
});
// Returns:
// {
// var: { value: 0.023, confidenceLevel: 0.99 },
// cvar: { value: 0.031 },
// sharpe: { value: 1.45 },
// maxDrawdown: { value: 0.082, period: '2023-10-01 to 2023-10-15' }
// }
`
Detect anomalies in financial transactions and market data.
`typescript
const result = await mcp.invoke('finance/anomaly-detect', {
transactions: [
{
id: 'tx-12345',
amount: 250000,
timestamp: '2024-01-15T14:30:00Z',
parties: ['CORP-A', 'CORP-B'],
metadata: { type: 'wire', currency: 'USD' }
}
],
sensitivity: 0.9,
context: 'aml'
});
// Returns:
// {
// anomalies: [{
// transactionId: 'tx-12345',
// score: 0.87,
// reasons: ['unusual_amount', 'first_time_counterparty', 'velocity_spike'],
// recommendation: 'review'
// }]
// }
`
Identify current market regime through pattern matching.
`typescript
const result = await mcp.invoke('finance/market-regime', {
marketData: {
prices: [100, 102, 99, 101, 103, 105, 104],
volumes: [1000000, 1200000, 900000, 1100000, 1300000, 1500000, 1400000],
volatility: [0.15, 0.16, 0.18, 0.17, 0.14, 0.13, 0.14]
},
lookbackPeriod: 252,
regimeTypes: ['bull', 'bear', 'high_vol', 'crisis']
});
// Returns:
// {
// currentRegime: 'bull',
// confidence: 0.82,
// similarHistoricalPeriods: ['2017-Q3', '2019-Q4'],
// transitionProbabilities: { bull: 0.75, bear: 0.15, high_vol: 0.10 }
// }
`
Automated regulatory compliance verification.
`typescript
const result = await mcp.invoke('finance/compliance-check', {
entity: 'BANK-001',
regulations: ['basel3', 'dodd_frank'],
scope: 'capital',
asOfDate: '2024-01-15'
});
// Returns:
// {
// compliant: true,
// metrics: {
// tier1Capital: { value: 0.125, requirement: 0.06, status: 'pass' },
// leverageRatio: { value: 0.05, requirement: 0.03, status: 'pass' }
// },
// warnings: ['tier1 buffer approaching minimum']
// }
`
Run stress testing scenarios on portfolios.
`typescript`
const result = await mcp.invoke('finance/stress-test', {
portfolio: {
holdings: [
{ symbol: 'SPY', quantity: 1000 },
{ symbol: 'QQQ', quantity: 500 }
]
},
scenarios: [
{ name: '2008 Financial Crisis', type: 'historical', shocks: {} },
{ name: 'Interest Rate +300bp', type: 'hypothetical', shocks: { rateShock: 0.03 } }
],
metrics: ['pnl', 'var_change', 'margin_call_risk']
});
`typescript
interface FinancialRiskConfig {
// Data paths
marketDataPath: string; // Path to market data cache
modelPath: string; // Path to trained models
// Compliance
auditLogPath: string; // Path for SOX-compliant audit logs
regulatoryReporting: boolean; // Enable auto-generated reports
// Performance
maxMemoryMB: number; // WASM memory limit (default: 1024)
maxCpuTimeSeconds: number; // Operation timeout (default: 60)
// Security
encryptionEnabled: boolean; // AES-256 encryption (default: true)
hsmKeyId?: string; // HSM key identifier for production
// Rate limiting
rateLimits: {
requestsPerMinute: number;
maxConcurrent: number;
};
}
`
This plugin is designed for financial regulatory compliance:
| Requirement | Implementation |
|-------------|----------------|
| PCI-DSS Compliance | No storage of PAN/CVV in plugin memory |
| SOX Compliance | Immutable audit logs for all risk calculations |
| Data Encryption | AES-256 for data at rest, TLS 1.3 in transit |
| Key Management | HSM or secure enclave for cryptographic keys |
| Segregation of Duties | Separate roles for trading, risk, and compliance |
`typescript`
const roles = {
TRADER: ['portfolio-risk', 'market-regime'],
RISK_MANAGER: ['portfolio-risk', 'anomaly-detect', 'stress-test', 'market-regime'],
COMPLIANCE_OFFICER: ['compliance-check', 'anomaly-detect'],
AUDITOR: ['compliance-check'], // Read-only with full audit access
QUANT: ['portfolio-risk', 'market-regime', 'stress-test']
};
All calculations are logged with:
- Timestamp with microsecond precision
- User ID and role
- Transaction IDs affected
- Portfolio state hash
- Model version used
- Input/output hashes for reproducibility
Logs are retained for 7 years per MiFID II requirements and available for regulatory inspection within 72 hours.
| Constraint | Value | Rationale |
|------------|-------|-----------|
| Memory Limit | 1GB max | Handle large transaction datasets |
| CPU Time Limit | 60 seconds | Allow complex risk calculations |
| No Network Access | Enforced | Prevent data exfiltration |
| No File System Write | Enforced | Analysis-only mode |
| Sandboxed Data Access | Enforced | No direct database queries |
All inputs are validated using Zod schemas:
- Stock symbols: /^[A-Z0-9.]{1,10}$/
- Position quantities: between -1 billion and 1 billion
- Transaction amounts: between -1 trillion and 1 trillion
- Batch limits: maximum 100,000 transactions per request
- Timestamps: ISO 8601 format, within reasonable date range
- Entity identifiers: Alphanumeric with limited special characters
`typescript`
const rateLimits = {
'portfolio-risk': { requestsPerMinute: 60, maxConcurrent: 5 },
'anomaly-detect': { requestsPerMinute: 100, maxConcurrent: 10 },
'stress-test': { requestsPerMinute: 10, maxConcurrent: 2 },
'market-regime': { requestsPerMinute: 120, maxConcurrent: 10 },
'compliance-check': { requestsPerMinute: 30, maxConcurrent: 3 }
};
| Metric | Target |
|--------|--------|
| Portfolio VaR calculation | <100ms for 10K positions |
| Transaction anomaly scoring | <5ms per transaction |
| Market regime classification | <50ms for 1-year history |
| Compliance check | <1s for full entity scan |
- micro-hnsw-wasm: Fast similarity search for historical pattern matchingruvector-sparse-inference-wasm
- : Efficient processing of sparse financial featuresruvector-gnn-wasm
- : Transaction network analysis for fraud detectionruvector-economy-wasm
- : Token economics and market microstructure modelingruvector-learning-wasm`: Reinforcement learning for adaptive risk thresholds
-
| Plugin | Description | Use Case |
|--------|-------------|----------|
| @claude-flow/plugin-legal-contracts | Contract analysis | Financial agreements, derivatives documentation |
| @claude-flow/plugin-healthcare-clinical | Clinical decision support | Healthcare portfolio analysis |
| @claude-flow/plugin-perf-optimizer | Performance optimization | High-frequency trading latency optimization |
MIT License
Copyright (c) 2026 Claude Flow
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