Fraud Detection System

What is Streaming Fraud?

Streaming fraud involves artificially inflating play counts through bots, fake accounts, or coordinated networks to manipulate royalty payments and chart positions.

Bot Streams

Automated accounts playing tracks repeatedly to inflate counts

Click Farms

Human-operated but inauthentic mass streaming operations

Playlist Stuffing

Fake playlists designed to fraudulently boost streams

Coordinated Networks

Multiple fake accounts working together to manipulate metrics

Seven Fraud Detection Dimensions

1. Stream Velocity Analysis

Analyzing the rate and pattern of stream accumulation.

Organic Growth: Gradual increase with natural fluctuations
Bot Activity: Sudden spikes, consistent 24/7 streaming
Time-of-Day Patterns: Humans stream less at night; bots don't sleep
Geographic Distribution: Natural vs concentrated in suspicious locations

2. Engagement Quality Metrics

Measuring authenticity of listener engagement.

Skip Rates: Bots skip less (<5%) vs humans (15-25%)
Completion Rates: Bots finish tracks more consistently
Listener Diversity: Unique listeners per stream ratio
Playlist Context: Legitimate vs suspicious playlist placement

3. Curator Authenticity

5-tier classification system for playlist curators.

Tier 1 - Official Spotify: Fraud rate <0.5%
Tier 2 - Verified Curators: Fraud rate <2%
Tier 3 - Established Independents: Fraud rate 5-10%
Tier 4 - Emerging Curators: Fraud rate 15-25%
Tier 5 - Suspicious Accounts: Fraud rate >40%

4. Network Analysis

Graph-based detection of coordinated fraud networks.

Sybil Attacks: Multiple fake identities from single entity
Bot Farms: Clusters of coordinated accounts
Cross-Playlist Patterns: Same bots across multiple playlists
Network Topology: Identifying fraud network structures

5. Behavioral Patterns

Distinguishing bot patterns from human behavior.

Listening Consistency: Bots are too consistent, humans vary
Time-Series Clustering: Grouping similar behavior patterns
Anomaly Detection: Identifying unusual engagement spikes
Human Variance Modeling: Expected natural fluctuations

6. Artist Authenticity Verification

Cross-platform verification of artist legitimacy.

Social Media Presence: Verified accounts, follower quality
Streaming History: Organic growth patterns over time
Release Schedule: Natural vs suspicious frequency
Fan Engagement: Quality of interactions and comments

7. Financial Impact Analysis

Calculating and documenting fraud-related losses.

Estimated Fraudulent Revenue: Dollar amount from fake streams
Royalty Impact: Effect on legitimate artist payments
Recovery Recommendations: Steps to reclaim losses
Legal Evidence: Court-admissible fraud documentation

Statistical Analysis Methods

Benford's Law

Natural data follows predictable first-digit distribution. Fraudulent data violates this law.

Expected: 30% of numbers start with 1, 18% with 2, etc.
Fraud Indicator: Uniform or non-Benford distribution
Application: Stream counts, follower numbers, engagement metrics

Time-Series Decomposition

Breaking down stream data into trend, seasonal, and residual components.

Trend: Long-term growth pattern
Seasonality: Repeating patterns (weekends, holidays)
Residual: Unexpected deviations (fraud spikes)

K-Means Clustering

Grouping similar fraud patterns for detection.

Cluster Analysis: Group accounts by behavior similarity
Fraud Signatures: Identify characteristic bot patterns
Outlier Detection: Find accounts that don't fit organic patterns

Isolation Forest

Machine learning anomaly detection algorithm.

Anomaly Score: How unusual an account's behavior is
Feature Analysis: 50+ engagement metrics
Ensemble Learning: Multiple models for robust detection

Machine Learning Models

XGBoost Fraud Detector

Target Accuracy: >90%

Model Type: Extreme Gradient Boosting
Training Data: 10,000 labeled playlists
Features: 50+ engagement metrics
Output: Binary classification (Fraudulent/Legitimate)
False Positive Target: <5%

Artist Authenticity Classifier

Target Accuracy: >85%

Model Type: Ensemble (Random Forest + XGBoost)
Classification: 5-tier system (see Curator Authenticity)
Cross-Platform Data: Spotify, YouTube, Instagram, Twitter
Historical Analysis: Growth patterns over 6+ months

Playlist Fraud Score (0-100)

0-20: Clean

Organic engagement, legitimate curator, no fraud indicators

21-40: Low Risk

Minor irregularities, mostly organic, monitoring recommended

41-60: Moderate Risk

Several fraud indicators, investigation strongly recommended

61-80: High Risk

Multiple fraud signals, likely fraudulent activity

81-100: Critical Risk

Definite fraud, bot network detected, report immediately

Bot Detection Indicators

Red Flags for Bot Activity

Skip rate <5% (humans skip 15-25%)
Consistent 24/7 streaming without breaks
Perfect completion rates (>95%)
Geographic clustering in known click farm locations
Benford's Law violations in stream counts
Account creation date clustering (bot farms)
Identical listening patterns across multiple accounts
Sudden, unexplained stream spikes

Implementation Timeline

Phase 2: Database Foundation (Weeks 2-3)

Create 5 database tables in cc93_data
Build Spotify API integration
Implement playlist data collection
Set up rate limiting and caching

Status: Planned for late October 2025

Phase 3: Fraud Detection Engine (Weeks 4-6)

Implement statistical analysis modules
Build behavioral pattern recognition
Create network analysis for coordinated fraud
Train XGBoost and artist authenticity models

Status: Planned for November 2025

Phase 4: Unified System (Weeks 7-8)

Combine AI detection + playlist fraud
Integrated dashboard UI
Combined risk scoring
Report generation

Status: Planned for December 2025

Feature in Development