A report titled "Research Report on the Chaos of Credit Black and Gray Industries and AI Prevention for Small and Micro Enterprises in China" was released on August 11 at a seminar and roundtable forum at the School of Economics and Management, Tsinghua University. The event, themed "When Black and Gray Industries Use AI: What New Challenges Does Financial Anti-Fraud Face?", explored the evolution of credit black and gray industries for small and micro enterprises, the application of artificial intelligence in financial anti-fraud, and multi-party collaborative governance.

At the forum, Lu Yao, Deputy Dean of Academic Affairs at the Schwarzman Scholars program at Tsinghua University, a professor at Tsinghua University's School of Economics and Management, Director of the China Financial Research Center at Tsinghua University's School of Economics and Management, and a recipient of the national high-level talent program, presented and analyzed the report. The report was jointly compiled by the China Financial Research Center at Tsinghua University's School of Economics and Management, XW Bank, and Xinhua Finance. It aims to systematically analyze the operational models and evolutionary trends of black and gray industries in the credit sector for small and micro enterprises, explore the application paths of AI technology in risk prevention, and provide a reference for improving the industry's anti-fraud capabilities and promoting the high-quality and sustainable development of inclusive finance.

In recent years, as financial services have become increasingly online and intelligent, the technical methods of financial black and gray industries have also evolved rapidly. While AI has improved the efficiency of financial services, it has also been used by some black and gray industries to generate fake materials, deepfake face-scanning videos, and other fraudulent activities. Financial anti-fraud is entering a more complex phase of technological confrontation.

From "Manual Fraud" to "AI Deep Empowerment": Black and Gray Industries Accelerate Full-Chain Evolution

The report systematically outlines the evolution path of credit black and gray industries for small and micro enterprises. From 2014 to 2018, the focus was mainly on forging paper materials and collusion between internal and external parties, displaying a relatively scattered manual fraud pattern. From 2019 to 2022, it entered a stage of digital empowerment, with methods like PS-forged electronic documents and batch acquisition of enterprise data emerging, and the division of labor within black and gray industry groups became clearer. Since 2023, with the rapid development of generative AI, new methods such as AI-generated false reports and deepfake face-scanning videos have appeared, intertwined with behaviors like loan fraud, money laundering, and malicious debt evasion.

Lu Yao stated that financial black and gray industries are evolving "from a workshop-style to an industrialized model, and from single-point attacks to full-chain collaboration." Traditional risk prevention models that rely on manual experience and fixed rules face new challenges, and financial institutions need to accelerate the transition from passive defense to active governance. The report believes that in the credit scenario for small and micro enterprises, current AI prevention still faces challenges such as insufficient data timeliness, non-standardized data sources and structures, and data fragmentation. This makes it difficult to form a complete enterprise risk profile, leading to the risk control problem of "difficulty in identifying individuals, and even greater difficulty in identifying gangs."

Wang Ping, an anti-fraud expert at XW Bank, stated that financial black and gray industries, with the help of AI, have fully entered the "industrialization" stage. This is reflected in three dimensions: in terms of methods, it has upgraded from "manual forgery" to "intelligent generation"; in terms of scale, it has expanded from "sporadic cases" to a "hundred-billion-level industrial chain"; and in terms of organizational form, it has upgraded from "scattered individuals" to "industrialized assembly lines."

From "Rule-Driven" to "Cognitive-Driven": AI Anti-Fraud Accelerates Upgrades

In response to the constantly evolving methods of black and gray industries, the report proposes three core principles for an AI prevention system. The first is to adhere to a "data + business" dual-wheel drive, integrating internal and external multi-source data with business understanding, and running through the entire process of feature construction, model training, and iterative optimization. The second is to strengthen penetration identification, using technologies such as knowledge graphs, association analysis, and graph neural networks to uncover actual controllers, hidden related parties, and gang relationships, restoring potential fraud chains. The third is to establish a real-time response and dynamic iteration mechanism, forming a closed loop through effect monitoring, early warning feedback, and strategy adjustment, so that prevention capabilities can continuously evolve along with changes in black and gray industry methods.

Lu Yao pointed out that with continuous technological progress, financial risk control is undergoing a constant upgrade from unsupervised anomaly detection and supervised learning real-time decision-making to reinforcement learning and multimodal intelligent understanding. "Large models are pushing risk control from 'rule-driven' to 'cognitive-driven,' allowing AI not only to identify risks but also to further understand risk logic and predict potential risks." Based on this idea, the report further proposes a five-layer AI prevention architecture covering data collection, feature engineering, model algorithms, decision execution, and continuous evolution. By integrating multi-source data, constructing risk labels, and applying technologies such as machine learning and knowledge graphs, it connects risk identification, decision execution, and dynamic optimization. This system also reflects the change in the technical path of financial anti-fraud: facing black and gray industries accelerated by AI, relying solely on static rules for "post-event interception" is no longer sufficient. Risk prevention is extending to real-time identification, correlation analysis, and proactive warning.

Technology, Institutions, and Ecosystem Synergy: Promoting Financial Anti-Fraud from "Single-Point Defense" to "Joint Governance"

The report also points out that financial black and gray industries have developed cross-platform, cross-institutional, and chain-like characteristics. Their governance is difficult to achieve by relying on a single financial institution or a single technical method and requires the formation of a more systematic collaborative governance mechanism. At the technical level, the integration of AI and data should be further deepened to continuously improve risk detection and early warning capabilities, making risk prevention more proactive. At the institutional level, relevant rules should be promoted to adapt to new fraudulent methods, further consolidating the governance foundation. At the ecosystem level, multi-party connectivity needs to be strengthened, forming a synergy in risk information, technical capabilities, governance experience, and public education.

"With collective efforts, nothing is impossible; with combined wisdom, nothing is unachievable." The governance of financial black and gray industries is not a solo effort by a single institution or field but requires collaborative co-governance across the entire industry and multiple entities. Lu Yao stated that only by advancing technology, institutions, and ecosystems simultaneously can the living space of financial black and gray industries be further compressed, safeguarding the security bottom line of inclusive finance. Small and micro enterprises are a vital part of the real economy. Their financing security is related not only to the risk management of financial institutions but also to the sustainability of inclusive financial services. As black and gray industries continuously upgrade their attack methods with the help of AI, financial anti-fraud also needs to persistently use "smart" strategies to counter "smart" attacks. The release of this report combines academic research with financial business practices, systematically reviewing the evolution of black and gray industries, AI prevention technologies, and collaborative governance mechanisms. Through this research, the research team hopes to promote the industry's risk prevention from passive response to active evolution, further enhancing the security, credibility, and sustainable development capabilities of inclusive finance in the digital era.