Data Collection
Gather a comprehensive dataset of financial transactions, including both normal and fraudulent activities, with detailed metadata such as transaction amounts, locations, and user profiles.
Model Fine-Tuning
Fine-tune GPT-4 on the transaction dataset to optimize its ability to identify patterns indicative of fraud and anomalies.
Real-Time Monitoring Framework
Develop a system that integrates the fine-tuned model to analyze transactions in real-time, flagging suspicious activities for further investigation.
Model Fine-Tuning
Optimize detection of fraud patterns using advanced machine learning techniques and transaction datasets.
Real-Time Monitoring
Integrate fine-tuned models to analyze transactions and flag suspicious activities instantly.
Evaluate model performance using precision, recall, and F1-score metrics for continuous improvement.
Performance Evaluation
Fraud Detection
Advanced system for identifying fraudulent transactions in real-time.
Comparative Analysis
Compare the fine-tuned GPT-4 with publicly available GPT-3.5 and other state-of-the-art fraud detection models to evaluate improvements in accuracy and efficiency.
Real-Time Monitoring Framework
Develop a system that integrates the fine-tuned model to analyze transactions in real-time, flagging suspicious activities for further investigation.
Expected Outcomes
This research aims to demonstrate that fine-tuning GPT-4 can significantly enhance its ability to detect fraudulent transactions and anomalous behaviors in real-time. The outcomes will contribute to a deeper understanding of how advanced AI models can be adapted for financial security applications. Additionally, the study will highlight the societal impact of AI in reducing financial losses, improving trust in digital transactions, and enabling more robust fraud prevention mechanisms.