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| """Pattern Analysis Agent - Detects known fraud patterns.""" | |
| from typing import Dict, List, Any | |
| class PatternAnalysisAgent: | |
| """Analyzes claims for known fraud patterns.""" | |
| def __init__(self): | |
| self.name = "PatternAnalysisAgent" | |
| self.version = "1.0" | |
| self.known_patterns = [ | |
| "rapid_succession_claims", | |
| "round_number_amounts", | |
| "weekend_incidents", | |
| "similar_claim_history" | |
| ] | |
| def process(self, claim_data: Dict[str, Any], historical_data: Dict[str, Any]) -> Dict[str, Any]: | |
| """Detect known fraud patterns.""" | |
| detected_patterns = [] | |
| pattern_scores = {} | |
| # Check for rapid succession | |
| if historical_data.get("prior_claims", 0) >= 3: | |
| detected_patterns.append("rapid_succession_claims") | |
| pattern_scores["rapid_succession"] = 0.7 | |
| # Check for round numbers | |
| amount = claim_data.get("claim_amount", 0) | |
| if amount % 1000 == 0 and amount > 0: | |
| detected_patterns.append("round_number_amounts") | |
| pattern_scores["round_numbers"] = 0.5 | |
| # Calculate overall pattern score | |
| overall_score = sum(pattern_scores.values()) / len(self.known_patterns) if pattern_scores else 0.0 | |
| return { | |
| "detected_patterns": detected_patterns, | |
| "pattern_scores": pattern_scores, | |
| "overall_pattern_score": min(overall_score, 1.0), | |
| "confidence": 0.85 | |
| } | |
| def get_trace(self) -> Dict[str, Any]: | |
| return { | |
| "agent": self.name, | |
| "version": self.version, | |
| "timestamp": "2024-12-31T01:00:00Z", | |
| "status": "completed" | |
| } | |