AI-Driven Synthetic Data Generation for Financial Product Development: Accelerating Innovation in Banking and Fintech through Realistic Data Simulation
Keywords:
AI-driven synthetic data, financial product development, banking innovationAbstract
Fintech and banking product development and testing are demanded by the quickly changing financial sector. Product creation and application are complicated by privacy, legality, and dataset restrictions. This paper explores how synthetic data produced by artificial intelligence could hasten the development of financial products. Synthetic data produced by GANs, VAEs, and Transformers meets privacy- and compliance-related standards. Money might be fabricated. Synthetic data allows fintech startups and financial institutions to test, grow, and assess creative concepts free from client data. Using realistic but synthetic datasets, companies may test more options—including catastrophic market conditions—to improve financial model resilience and endurance.
This research showed the financial product development needed for fictitious data. Analyze synthetic minority oversampling, VAEs, and GANs in both positive and negative aspects. We evaluate statistical similarity, privacy, practical relevance, synthetic data quality, and utility. Talking about artificial intelligence-driven synthetic data in banking ethics and legality and the requirement of transparent and understandable AI models for compliance and trust. It addresses fintech and financial firms using synthetic data to create and test creative ideas, hence lowering time-to-market and development costs.
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