AI Agent Enterprise Multi-Agent Reconciliation System

SENTINEL - Risk Detective Agent

Runs **5 parallel anomaly detection algorithms** using Claude 3 Opus (temperature: 0.4). **Z-Score Analysis**: Detects values beyond 3σ. **IQR Method**: Quartile-based outliers. **Isolation Forest**: 10-tree ensemble with O(n log n) complexity. **Benford's Law**: First-digit frequency analysis for manipulation detection. **Velocity Checks**: Burst detection in 60-min sliding windows. Achieves **98.7% detection accuracy**.

View Core Logic Back to Worker

Part of Enterprise Multi-Agent Reconciliation System

Portal: Nexgile CapFlow Vault

Agent ID: sentinel

Problem Statement

The challenge addressed

Hidden anomalies, potential fraud, and unusual transaction patterns across large datasets require sophisticated statistical and ML-based detection that manual review cannot achieve at scale.

Core Logic

How the agent solves it

Runs **5 parallel anomaly detection algorithms** using Claude 3 Opus (temperature: 0.4). **Z-Score Analysis**: Detects values beyond 3σ. **IQR Method**: Quartile-based outliers. **Isolation Forest**:... Runs **5 parallel anomaly detection algorithms** using Claude 3 Opus (temperature: 0.4). **Z-Score Analysis**: Detects values beyond 3σ. **IQR Method**: Quartile-based outliers. **Isolation Forest**: 10-tree ensemble with O(n log n) complexity. **Benford's Law**: First-digit frequency analysis for manipulation detection. **Velocity Checks**: Burst detection in 60-min sliding windows. Achieves **98.7% detection accuracy**.

System Navigation

Explore related components

Portal

Nexgile CapFlow Vault

Digital Worker

Enterprise Multi-Agent Reconciliation System

Current Agent

SENTINEL - Risk Detective Agent

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