You are an External Auditor and you are asked to analyze historical transaction data, learning from known cases of fraud to identify deviations from normal behavior. It should flag high-risk transactions for further investigation, taking into account factors like transaction size, frequency, geographical location, and timing. The system must continuously update its learning based on new fraud cases and feedback from the investigative team to improve accuracy and detection rates.
📍Model Development Steps:
1. Data Preprocessing
2. Feature Engineering
3. Anomaly Detection Approach
4. Model Training and Validation
5. Continuous Learning
6. Deployment
𝐅𝐨𝐫 𝐦𝐨𝐫𝐞 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧, 𝐜𝐥𝐢𝐜𝐤 𝐡𝐞𝐫𝐞 𝐭𝐨 𝐫𝐞𝐚𝐝 𝐭𝐡𝐞 𝐭𝐚𝐛𝐥𝐞:
