anti-corruption-investigation

name	anti-corruption-investigation
description	Advanced anti-corruption investigation system for analyzing chat logs and communications to detect suspicious patterns, corruption indicators, and relationship networks. Supports Chinese and English chat logs in JSON/TXT formats, handles million-scale datasets, and provides human-friendly relationship analysis with evidence-backed conclusions.

Anti-Corruption Investigation v7.0

What's New in v7.0

Multi-Language Pattern Detection

• Chinese Patterns: Financial corruption, power abuse, secret meetings, collusion
• English Patterns: Evidence destruction, insider trading, pressure manipulation, enterprise fraud
• Universal Enterprise Fraud Detection: SPE, accounting manipulation, financial metrics manipulation
• Semantic Pattern Matching: Detects implicit expressions and euphemisms

Social Network Analysis

• Person Profile Analysis: Comprehensive profiling including role detection, activity patterns, and risk assessment
• Intermediary Detection: Automatically identifies bridge persons connecting corruption networks
• Community Detection: Discovers corruption groups based on communication patterns
• Influence Analysis: Ranks individuals by network influence and centrality
• Connection Path Analysis: Finds shortest paths between high-risk individuals

Validation Framework

• Pattern Validation: Validates detection accuracy against known corruption patterns
• Report Validation: Ensures report completeness and quality
• False Positive Control: Estimates and controls false positive rates
• Continuous Improvement: Generates recommendations for pattern enhancement

When to Use This Skill

Use when analyzing chat logs, messages, or communications for:

• Corruption detection: Financial corruption, power abuse, secret meetings, collusion
• Relationship analysis: Identifying key players, corruption networks, intermediaries
• Social network analysis: Understanding person profiles, influence, and group structures
• Large-scale analysis: Processing 100K+ messages efficiently
• Evidence gathering: Extracting specific evidence for relationships
• Risk assessment: Evaluating corruption risk levels

Quick Start

Basic Analysis

from anti_corruption import ChatAnalyzer

# Analyze chat data
messages = [...]  # Load your messages
analyzer = ChatAnalyzer(messages)
results = analyzer.analyze()

# View results
print(f"Risk Level: {results['risk_level']}")
print(f"Suspicious Messages: {len(results['suspicious_messages'])}")

Multi-Language Pattern Detection

from multi_lang_patterns import analyze_text, analyze_email

# Analyze text in any language
result = analyze_text("We need to delete these documents before audit")
print(f"Risk Score: {result['risk_score']}")
print(f"Categories: {result['categories']}")

# Analyze email with enterprise fraud detection
email_data = {
    'sender': '[e-mail]',
    'receiver': '[e-mail]',
    'subject': 'Q4 Results',
    'content': 'We need to hit the target number.',
    'title': 'CFO'
}
result = analyze_email(email_data)
print(f"Risk Level: {result['risk_level']}")

Relationship Analysis

from anti_corruption import RelationshipAnalyzer

# Analyze relationships
analyzer = RelationshipAnalyzer(messages)
relationships = analyzer.analyze()

# View top relationships
for rel in relationships['top_relationships'][:10]:
    print(f"{rel['person_a']} ↔ {rel['person_b']}")
    print(f"  Type: {rel['relationship_type']}")
    print(f"  Evidence: {len(rel['evidence'])} items")
    print(f"  Risk: {rel['risk_level']}")

Social Network Analysis

from anti_corruption import SocialNetworkAnalyzer

# Analyze social network
analyzer = SocialNetworkAnalyzer(messages)
results = analyzer.analyze()

# View person profiles
for name, profile in results['person_profiles'].items():
    print(f"{name}: {profile['primary_role']} - {profile['risk_level']}")

# View intermediaries
for inter in results['intermediaries'][:5]:
    print(f"Intermediary: {inter['name']} (Score: {inter['brokerage_score']})")

# View communities
for comm in results['communities']:
    print(f"Community: {', '.join(comm['members'][:5])}")

Validation

from case_validator import validate_analysis, generate_validation_report

# Validate analysis results
validation = validate_analysis(analysis_results)
print(f"Detection Accuracy: {validation.detection_accuracy:.1%}")
print(f"False Positive Rate: {validation.false_positive_rate:.1%}")

# Generate validation report
report = generate_validation_report(validation, 'validation_report.txt')

Core Scripts

anti_corruption.py

Unified analysis tool with all features.

Usage:

# Basic corruption analysis
python anti_corruption.py analyze input.jsonl report.json

# Relationship analysis
python anti_corruption.py relationships input.jsonl relationships.json --text-report report.txt

# Social network analysis
python anti_corruption.py social-network input.jsonl social_network.json --text-report social_report.txt

# Full analysis with all features
python anti_corruption.py full input.jsonl output_dir/

Commands:

• analyze: Basic corruption pattern detection
• relationships: Relationship network analysis
• social-network: Social network and person profile analysis
• full: Run all analyses

multi_lang_patterns.py

Multi-language pattern detection module.

Features:

• Automatic language detection (Chinese, English, Mixed)
• Direct pattern matching for corruption indicators
• Semantic pattern matching for implicit expressions
• Enterprise fraud specific patterns (SPE, accounting manipulation)
• Risk scoring based on pattern matches

Usage:

from multi_lang_patterns import MultiLangPatternMatcher, EnterpriseFraudDetector

# Pattern matching
matcher = MultiLangPatternMatcher()
matches = matcher.match_patterns(text)
summary = matcher.get_summary(matches)

# Enterprise fraud detection
detector = EnterpriseFraudDetector()
result = detector.analyze_email(email_data)

case_validator.py

Validation framework for analysis results.

Features:

• Pattern detection validation
• False positive rate estimation
• Report completeness checking
• Improvement recommendations

Usage:

from case_validator import PatternValidator, ReportValidator

# Validate analysis
validation = PatternValidator.validate_analysis(results)

# Validate report
report_check = ReportValidator.validate_report(report)

Data Format

Input Format (JSONL)

{"timestamp": "2024-01-15T14:30:00", "sender": "张三", "receiver": "李四", "content": "那笔钱准备好了吗？"}
{"timestamp": "2024-01-15T14:31:00", "sender": "李四", "receiver": "张三", "content": "已经准备好了"}

Input Format (TXT)

[2024-01-15 14:30:00] 张三 -> 李四: 那笔钱准备好了吗？
[2024-01-15 14:31:00] 李四 -> 张三: 已经准备好了

Output Format

Social Network Analysis Output

{
  "person_profiles": {
    "张三": {
      "name": "张三",
      "message_count": 150,
      "contact_count": 8,
      "contacts": ["李四", "王五", ...],
      "primary_role": "official",
      "detected_roles": ["official", "business"],
      "suspicious_message_count": 25,
      "corruption_patterns": {
        "financial_corruption": 15,
        "power_abuse": 10
      },
      "risk_score" 
       
       
         
         
           
      
    
  
   
    
       
       
       
       
       
    
  
   
    
       
         
       
       
       
    
  

Pattern Categories

Financial Corruption

• Chinese: 转账, 汇款, 回扣, 贿赂, 好处费
• English: kickback, bribe, hidden payment, secret fee

Evidence Destruction

• Chinese: 删除, 销毁, 清理, 不留痕迹
• English: delete, destroy, shred, clean up, off the record

Insider Trading

• English: stock option, insider information, before announcement

Enterprise Fraud

• SPE, off-balance sheet, mark-to-market
• Aggressive accounting, earnings management
• EBITDA manipulation, pro forma adjustments

Pressure Manipulation

• English: pressure, hit the target, make it happen
• Adjust numbers, bridge the gap, find a way

Version History

• v7.0: Added multi-language pattern detection, enterprise fraud patterns, validation framework
• v6.0: Added social network analysis, person profiling, intermediary detection
• v5.0: Refactored for clarity, human-friendly output, improved performance
• v4.0: Added relationship network analysis
• v3.0: Large-scale processing support
• v2.0: Semantic pattern matching
• v1.0: Initial release with keyword-based detection