Dynamic Form Rendering with AI Driven Schema Generation and Identity Document Pre Fill: A Cross Market Approach
Year: 2025
Type: article
Abstract: Financial institutions face a complex challenge when onboarding customers across multiple jurisdictions. Each market has unique regulatory requirements, cultural expectations, and technical constraints that make a one-size-fits-all approach impossible. This research presents a comprehensive solution that combines artificial intelligence with dynamic form generation to address these challenges. Our approach leverages JSON Schema as its foundational architecture, enabling forms to adapt their fields, validation rules, and visual presentation based on market-specific requirements. By integrating advanced optical character recognition with machine learning algorithms, the system extracts verified information from government-issued identification documents and automatically populates relevant form fields. This dramatically reduces the time customers spend on applications while improving data accuracy. Field testing with three major financial institutions over six months showed remarkable results. Application completion rates increased from 67% to 89%, while the average time to complete an application dropped from 23.5 minutes to just 8.2 minutes. Perhaps more significantly, data entry errors requiring manual review decreased from 12% to 3%, substantially reducing operational costs. The modular architecture we developed allows financial institutions to expand into new markets rapidly while maintaining compliance with local regulations. As digital identity frameworks continue to evolve globally, this approach provides a foundation for integrating emerging technologies while supporting traditional document-based verification methods.
Author: Naveen Kumar Chandu
Institution: Jawaharlal Nehru Technological University, Hyderabad
Language: English
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4412425179