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| """Anomaly Detection Agent - Identifies unusual behaviors.""" | |
| from typing import Dict, List, Any | |
| class AnomalyDetectionAgent: | |
| """Detects anomalies in claim data.""" | |
| def __init__(self): | |
| self.name = "AnomalyDetectionAgent" | |
| self.version = "1.0" | |
| self.anomaly_threshold = 0.6 | |
| def process(self, claim_data: Dict[str, Any], historical_data: Dict[str, Any]) -> Dict[str, Any]: | |
| """Detect anomalies in claim.""" | |
| anomalies = [] | |
| anomaly_score = 0.0 | |
| # Check claim amount vs historical average | |
| claim_amount = claim_data.get("claim_amount", 0) | |
| if claim_amount > 10000: | |
| anomalies.append("unusually_high_amount") | |
| anomaly_score += 0.4 | |
| # Check fraud flag | |
| if historical_data.get("fraud_flag", False): | |
| anomalies.append("previous_fraud_history") | |
| anomaly_score += 0.5 | |
| # Normalize score | |
| anomaly_score = min(anomaly_score, 1.0) | |
| return { | |
| "anomalies_detected": anomalies, | |
| "anomaly_score": anomaly_score, | |
| "is_anomalous": anomaly_score >= self.anomaly_threshold, | |
| "confidence": 0.82 | |
| } | |
| def get_trace(self) -> Dict[str, Any]: | |
| return { | |
| "agent": self.name, | |
| "version": self.version, | |
| "timestamp": "2024-12-31T01:00:00Z", | |
| "status": "completed" | |
| } | |