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Computer Science
Cybersecurity
Fraud Detection and Prevention
1. Introduction to Fraud
2. Data and Feature Engineering for Fraud Detection
3. Fraud Detection Methodologies
4. Machine Learning Models in Depth
5. Fraud Prevention Strategies
6. Operationalizing Fraud Systems
7. Legal, Ethical, and Regulatory Frameworks
8. Emerging Trends and Future Challenges
Data and Feature Engineering for Fraud Detection
Data Sources
Transactional Data
Payment Transactions
Account Transactions
Transfer Records
Purchase Histories
Refund Records
User Profile Data
Demographic Information
Account Creation Details
Account Status History
Contact Information
Verification Status
Behavioral Data
Clickstream Data
Navigation Patterns
Session Duration
Page Views
Device Fingerprinting
Device Identification
Browser Information
Operating System Data
Screen Resolution
Geolocation Data
IP Address Location
GPS Coordinates
Time Zone Information
Third-Party Data
Credit Bureau Data
Credit Scores
Credit History
Credit Inquiries
Public Records
Court Records
Business Registrations
Property Records
Social Media Data
Profile Verification
Social Network Analysis
Activity Patterns
Network and Infrastructure Data
Server Logs
Network Traffic Data
Security Event Logs
Data Quality and Preprocessing
Data Quality Assessment
Completeness Analysis
Accuracy Validation
Consistency Checks
Timeliness Evaluation
Missing Data Handling
Missing Data Patterns
Imputation Methods
Mean Imputation
Median Imputation
Mode Imputation
Forward Fill
Backward Fill
Deletion Strategies
Listwise Deletion
Pairwise Deletion
Data Normalization
Min-Max Scaling
Z-Score Standardization
Robust Scaling
Unit Vector Scaling
Outlier Detection and Treatment
Statistical Methods
Z-Score Method
IQR Method
Modified Z-Score
Machine Learning Methods
Isolation Forest
Local Outlier Factor
Treatment Strategies
Removal
Transformation
Capping
Categorical Data Encoding
One-Hot Encoding
Label Encoding
Target Encoding
Binary Encoding
Frequency Encoding
Data Deduplication
Exact Matching
Fuzzy Matching
Record Linkage
Feature Engineering
Domain-Specific Features
Transaction Features
Amount-Based Features
Frequency Features
Location Features
Account Features
Age Features
Activity Features
Balance Features
Time-Based Features
Temporal Patterns
Hour of Day
Day of Week
Month of Year
Seasonality
Recency Features
Time Since Last Transaction
Time Since Account Creation
Time Since Last Login
Frequency Features
Transactions per Hour
Transactions per Day
Login Frequency
Velocity Features
Transaction Velocity
Amount Velocity
Location Velocity
Aggregation Features
User-Level Aggregates
Total Transaction Amount
Average Transaction Amount
Transaction Count
Unique Merchant Count
Merchant-Level Aggregates
Transaction Volume
Customer Count
Fraud Rate
Chargeback Rate
Device-Level Aggregates
Account Count per Device
Transaction Volume per Device
User Count per Device
Location-Level Aggregates
Transaction Count per Location
User Count per Location
Velocity per Location
Network and Graph Features
Node Features
Degree Centrality
Betweenness Centrality
Closeness Centrality
Edge Features
Connection Strength
Transaction Frequency
Amount Flow
Community Features
Community Membership
Community Size
Inter-Community Connections
Feature Selection
Filter Methods
Correlation Analysis
Chi-Square Test
Mutual Information
Wrapper Methods
Forward Selection
Backward Elimination
Recursive Feature Elimination
Embedded Methods
LASSO Regularization
Ridge Regularization
Tree-Based Feature Importance
Dimensionality Reduction
Principal Component Analysis
Linear Discriminant Analysis
t-SNE
UMAP
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1. Introduction to Fraud
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3. Fraud Detection Methodologies