The objective of this project is to design an AI-based audit system that uses machine learning to detect anomalies in system activities and transactions. The system assists auditors by automatically identifying suspicious patterns that may indicate compliance violations or fraud.
Study audit processes and limitations of rule-based auditing.
Understand anomaly detection techniques using machine learning.
Design an intelligent audit architecture integrating AI models.
Collect and preprocess audit log and transaction datasets.
Train anomaly detection models on historical data.
Evaluate model performance using simulated anomalies.
Integrate alerting mechanisms for detected irregularities.
Visualize audit insights using dashboards.
Test system effectiveness across multiple scenarios.
Analyze false positives and tuning strategies.
Document AI benefits, risks, and ethical considerations.
