Accurate credit risk assessment is essential for onboarding clients in wealth
management, but legal and confidentiality constraints often prevent direct access to
transaction data. To address this, we propose using synthetic data generation to
create realistic financial data patterns while preserving client privacy. Our AI-powered solution generates synthetic data that mirrors real-world transaction
behaviors, reflecting aspects like financial practices, credit utilization, and payment
habits. This enables effective credit risk evaluation without breaching confidentiality.
Our system simulates ongoing transaction behaviors, continuously monitoring trends
such as shifts in credit usage. Rigorous validation processes, including pattern
comparison, statistical analysis, expert reviews, anomaly detection, and pilot testing,
ensure the synthetic data remains realistic and reliable for credit assessments.
