At the 8th China Financial Technology Forum, held during the 2026 China International Fair for Trade in Services (CIFTIS), Li Mengyuan, Financial Consumer Protection Product Director at Lingyan Tech, delivered a keynote speech on "Exploring and Practicing AI-Enabled Scenarios for Financial Institution Consumer Protection." The presentation systematically outlined the construction approach and real-world implementation of their digital intelligence consumer protection platform, followed by exchanges with representatives from domestic and international financial institutions and technology firms.

As consumer protection regulations continue to take effect, the scope of consumer protection work has evolved from isolated complaint handling into a full-cycle mission covering marketing communications, product design, complaint resolution, remediation tracing, and regulatory evaluations. With the National Financial Regulatory Administration institutionalizing routine consumer protection assessments, establishing a seamless capability chain spanning "prevention before incidents, handling during incidents, and rectification after incidents" has become essential for financial institutions. Against this backdrop, leveraging digital intelligence to embed consumer protection capabilities across all business lines has emerged as a shared challenge for banks and other financial entities alike.

Regulatory Tightening Transforms Consumer Protection from Complaint Handling to Full-Cycle Mission

Li noted that Lingyan Tech has accumulated seven years of practical experience in financial consumer protection, an area that was among the earliest to adopt AI empowerment, has seen the broadest application, and delivers substantial customer value. Financial institutions currently face multiple challenges: first, regulatory requirements are becoming increasingly stringent and detailed; second, consumer awareness of rights is growing, leading to a rapid rise in cases involving comprehensive fees and other issues, with many institutions reporting regulatory complaints surging more than tenfold; third, some internal business departments lack a proper understanding of consumer protection, viewing it as solely the responsibility of the consumer protection department and failing to cooperate closely; and fourth, many institutions remain predominantly reactive, handling issues only after they arise, making it difficult to adapt to current demands.

In response, Lingyan Tech proposes a bottom-up approach to shift consumer protection philosophy from post-hoc handling to proactive prevention. The strategy involves focusing on objectives at every stage of the process, mining historical data from complaints and reviews, deploying AI tools and intelligent agents to empower each link, and establishing dashboards, command centers, and quantitative metrics. This approach encourages business departments to participate actively, transforming consumer protection from a single-department responsibility into an enterprise-wide operational baseline, while integrating business, risk, customer, and compliance data to build capabilities that empower all business operations.

Three Pillars Underpin a Next-Generation Consumer Protection Control System

Li explained that addressing current challenges and adapting to future trends requires building a new-generation consumer protection control system characterized by digitization, intelligence, and full-process management. Drawing on seven years of practice and implementation experience across multiple financial institutions, Lingyan Tech proposes a framework that combines data capabilities anchored by consumer protection tagging with AI capabilities to drive full-process control and business empowerment. This system rests on three pillars: first, establishing a robust full-process control platform that provides consumer protection departments with actionable tools, building systematic capabilities for pre-incident consumer protection reviews, in-incident work order handling, and post-incident complaint rectification, while continuously accumulating consumer protection data assets and developing upward-facing platforms that empower bank-wide services based on these assets to support product iteration and service quality enhancement; second, developing a regulatory assessment self-evaluation system that uses self-assessment tools to create routine evaluation capabilities, continuously iterating in alignment with regulatory requirements; and third, synergizing data capabilities—represented by consumer protection tagging—with AI capabilities to build a data-driven, continuously evolving mechanism that achieves win-win outcomes for consumer protection and high-quality business development.

Tagging System and Full-Cycle Intelligent Applications Go Live

On the data infrastructure front, Lingyan Tech has built a multi-tier consumer protection tagging system. The first four tiers correspond to banking business categories, business lines, business products, and business processes, forming the banking business ontology and its digital representation. The fifth tier is mounted on different vertical domains, such as knowledge scenarios in the consumer protection field, creating a comprehensive tagging system and accumulated financial business knowledge. At one joint-stock bank, Lingyan Tech has generated approximately 3,000 tagged scenarios covering the bank's entire business process flows and historical complaint scenarios.

Using the tagging system as a bridge, Lingyan Tech attaches review rules in pre-incident scenarios to form a review knowledge base; attaches job responsibilities and handling strategies in in-incident scenarios to build an efficiency-enhancing knowledge framework; and attaches complaint causes and demands in post-incident scenarios to establish a traceability and attribution knowledge base. By extending these capabilities, they become critical carriers of intelligent agent scenario value and lay the groundwork for full-process intelligent agent management.

In the pre-incident intelligent review stage, regulatory requirements mandate that all materials undergo review. Faced with multimodal data—such as marketing posters, product descriptions, and activity rules—Lingyan Tech's intelligent review platform leverages large model multimodal capabilities for rapid ingestion and recognition. The tagging-based review knowledge base supports the large model in swiftly identifying risk points, applicable rules, penalty grounds, and revision suggestions within review materials, generating comprehensive review reports. Review staff only need to make necessary adjustments to the reports before completing approvals, dramatically improving efficiency.

In the in-incident complaint handling stage, after customer service receives a complaint, call recordings are transcribed in real time and rapidly converted into the tagging system via AI. With job roles and optimal strategies attached, the platform immediately pushes the best solution to the customer service dashboard or directly to robots for automatic customer feedback. At a joint-stock bank served by Lingyan Tech, credit card operations generate 2 million annual work orders; by selecting 200 scenarios—out of 700—to refine strategies, significant labor savings were achieved. Another joint-stock bank, through intelligent dispatch, achieved 60-minute response times for all complaints and 48-hour case closure, with a 98% completion rate and AI recognition accuracy exceeding 90% in the industry.

In the post-incident stage, the platform aggregates complaints from all channels, then uses clustering and trend analysis to generate dashboards and hotspots, identifying weak points and high-frequency complaint-prone products. For example, personal loan operations are a major source of consumer protection complaints; the platform can analyze weaknesses across the full process, precisely target rectification, and monitor closure. One joint-stock bank updates its mobile banking app every three months with product issues identified through complaint traceability, preventing similar issues from repeatedly harming consumers and moving risks forward into product development. Another bank used attribution findings to build a customer AUM attrition early-warning model, reducing customer churn risk. Through intelligent attribution, precise rectification, and closed-loop monitoring, the platform delivers full-process empowerment.

Consumer Protection Intelligent Agent Matrix and Market Recognition

Li stated that the consumer protection field is entering the intelligent agent era, requiring substantial data support at the foundation. In addition to the tagging library, Lingyan Tech has built internal and external regulatory libraries, knowledge point libraries, case libraries, and complaint libraries. Built on this knowledge base, a series of intelligent agents has been developed, including tag annotation, regulatory comparison, review opinion generation, report generation, consumer protection assessment, and intelligent Q&A. Regulatory comparison previously relied on manual expert interpretation but can now be automatically parsed into knowledge points by AI agents—a typical AI for Data application scenario. Based on these consumer protection intelligent agents, the platform broadly empowers pre-incident, in-incident, and post-incident full-cycle scenarios.

With 25 years of history, Lingyan Tech serves approximately 280 financial institutions in China, accompanying the industry through informatization, digitization, and intelligent transformation. The company is recognized as a trustworthy partner that understands business operations and possesses AI innovation capabilities. In the IDC 2025 China Banking IT Solutions Market Share Report, Lingyan Tech ranked first in the consumer protection business segment. Moving forward, Lingyan Tech will build on this foundation, continuing to deepen its focus on financial consumer protection, using the consumer protection tagging system as the data backbone and AI intelligent agents as the capability engine, continuously improving the full-cycle digital intelligence consumer protection platform covering pre-incident review, in-incident handling, and post-incident rectification.