复贷率93.9%的背后：奇富科技(QFIN.US)以AI创极简服务，赢长期信任

Core Insights - The financial technology industry is experiencing intensified competition driven by consumer demand for high-quality credit services that emphasize speed, accuracy, and stability [1] - QFIN's platform has achieved a user re-borrowing rate exceeding 90% for nine consecutive quarters, significantly higher than the industry average, indicating strong user satisfaction with its streamlined service [1] Group 1: Simplified Processes - QFIN has compressed the entire loan process from application to disbursement to the industry's limits, enhancing efficiency through deep user behavior data analysis and AI technology [2] - The application process has been simplified to just two steps, significantly reducing abandonment rates, with returning users completing the process in under two minutes [2] - The platform's AI risk control model allows for 99.5% of applications to be approved almost instantly, with an average approval time reduced from over three minutes to as fast as 20 seconds [2] Group 2: Exceptional Service - QFIN has established a 24/7 customer service system that provides millisecond-level response times, with AI handling over 80% of common inquiries [3] - The proactive service system includes automatic reminders for repayment and credit limit expirations, reducing user overdue rates by approximately 12% [3] - User feedback indicates that 72% of repeat borrowers appreciate the quick customer response, and 65% value timely reminders to avoid overdue payments [3] Group 3: Long-term Trust - High re-borrowing rates reflect users' long-term trust in the platform, supported by both technological capabilities and quality service [4] - QFIN's dynamic credit system allows 85% of long-term users to receive credit limit increases, reinforcing the idea that increased usage leads to better service [4] - The platform maintains a balance between convenience and risk control, with overdue rates for repeat borrowers being 20% lower than for new users, demonstrating effective risk management [4]