AI in Financial Applications
Detecting fraud and anomalies in financial transactions
About This Project
This project focuses on developing a sophisticated model for detecting fraud and anomalies in financial transaction datasets. Using advanced machine learning techniques, the system identifies suspicious patterns that may indicate fraudulent activity, unusual spending behavior, or security breaches. The project includes comprehensive analysis of transaction patterns, visualization of detected anomalies, and a detailed reporting system to communicate findings to stakeholders in an accessible and actionable format.
Core Concepts
- Anomaly detection algorithms
- Financial transaction analysis
- Pattern recognition in time series data
- Fraud detection strategies
- Risk scoring methodologies
- Feature engineering for financial data
Key Knowledge/Skills
- Financial data analysis
- Time series anomaly detection
- Imbalanced dataset handling
- Statistical pattern recognition
- Risk assessment frameworks
- Financial contexts and regulations
Coursework Covered
AI in Finance and FinTech (or for Business & Industry)