Yiren Digital Ltd. (NYSE: YRD) (“Yiren Digital” or the “Company”), a leading company specializing in financial technology and artificial intelligence innovation across multiple industries in China and global markets, today announced measurable operating efficiency improvements as it continues to deploy AI agents across core enterprise workflows. Broader AI adoption is reducing manual intervention, increasing workforce productivity and creating greater operating leverage by automating high-volume processes across multiple business functions.
These deployments are a key component of Yiren Digital’s “All-in-AI” strategy and its broader transition from AI-assisted productivity toward agent-driven execution. By embedding AI agents into core workflows, the Company is creating reusable operating capabilities that can be deployed across its businesses, supporting greater efficiency and reducing the cost of extending automation into new functions.
“Our objective is not simply to automate individual tasks, but to fundamentally improve how work is performed across the enterprise,” said Mr. Ning Tang, Chairman and Chief Executive Officer of Yiren Digital. “As AI agents take on more of our high-volume, demanding workflows, the productivity gains are becoming a structural part of how we run the business, not a one-time efficiency project. We will continue to deepen AI integration across our existing businesses while extending reusable capabilities into additional verticals.”
The AI deployments are supported by the Company’s proprietary enterprise AI architecture, including MagiCube 2.0, its upgraded multi-agent platform. The platform provides common infrastructure for agents deployed across marketing, customer service, capital operations, risk management, compliance and research and development, with more than 10 reusable foundational capabilities, supporting enterprise-wide execution.
Measurable Operating Impact
Lower manual intervention: The human handling rate in asset-recovery operations decreased from 45.0% to 24.9%, representing a 20.1-percentage-point decline, an approximately 44.6% relative reduction in manual intervention.
Higher staff productivity: The number of service tickets handled per asset-recovery staff member within the applicable Month 1 workflow increased from 358 to 525, an improvement of approximately 47%.
Expanded agent adoption: AI agents accounted for 81% of service tickets within eligible Day 1 asset-recovery workflows in 2025, up from 50% in 2024. The Company also deployed AI agents selectively in later-stage workflows, accounting for 20% of eligible service tickets at Day 4, 14% at Day 16 and 20% at Month 2. Each percentage is calculated separately for the relevant stage and should not be interpreted as a sequential adoption trend.
Enterprise-wide reuse: MagiCube 2.0 supports agent deployment across six enterprise functions, allowing the Company to apply common AI capabilities to a broader range of regulated and high-volume workflows.
Enterprise-scale AI execution: The Fengchao AI voice agent processes approximately 1,500 hours of real-time speech-to-text activity each day. The LingShu intelligent marketing platform executes more than 1,700 tasks daily and generates individualized communication content in an average of 0.6 seconds.
Building Enterprise Operating Leverage Through AI
As AI deployment expands across the enterprise, Yiren Digital is increasingly shifting repetitive, high-volume tasks from human-assisted processes toward agent-driven execution. By combining AI agents with centralized orchestration and governance, the Company is improving operating consistency, strengthening workforce productivity and creating reusable capabilities that increase operating leverage as AI is deployed across additional business functions.
Yiren Digital plans to continue expanding agent-driven workflows across its credit and insurance operations, as part of its ongoing All-in-AI strategy, while strengthening the shared architecture and governance that support enterprise-wide AI deployment. These capabilities are designed to scale across multiple use cases and provide a foundation for the Company’s broader expansion into AI application-layer opportunities, including AI entertainment and AI-assisted language learning.
