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While real data can be very valuable, it may not be easily available. At J.P. Morgan AI Research, we conduct research and develop algorithms to generate realistic Synthetic Datasets, with the aim of advancing AI research and development in financial services. Feel free to explore our available datasets on the lefthand panel.
Below is a sample process we devised at J.P. Morgan AI Research to generate synthetic financial datasets. To learn more about the challenges and opportunities in generating data in finance, please read Generating Synthetic Data in Finance: Opportunities, challenges and pitfalls.
Step 1: Compute metrics for the real data
Step 2: Develop a Generator (may be statistical methods or an agent-based simulation)
Step 3: (Optional) Calibrate the Generator using the real data
Step 4: Run the Generator to generate synthetic data
Step 5: Compute metrics for the synthetic data
Step 6: Compare the metrics of the real data and synthetic data
Step 7: (Optional) Refine the Generator to improve against comparison metrics
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