Core Insights - The 10th Huawei Connect Conference (HUAWEI CONNECT 2025) was held in Shanghai, focusing on AI acceleration in financial technology (FinTech) innovation [1] - The chief algorithm scientist of Qifu Technology, Fei Haojun, highlighted three core challenges faced by microfinance: low data transparency, diverse enterprise types leading to disorganized data formats, and traditional risk control models lacking deep causal and logical reasoning capabilities [1] Company Developments - Qifu Technology is upgrading its core capabilities in perception, cognition, and logical decision-making to build a new generation of intelligent decision engines [1] - The company has developed a proprietary multimodal large model (MLLM) specifically for the financial sector, which excels in parsing vast amounts of non-standard documents like bank statements and income proofs, showing significant advantages in accuracy and recall compared to mainstream open-source models [1] - Qifu Technology has created three knowledge graphs covering 33 million enterprise entities, including enterprise relationship graphs, product graphs, and event graphs, enabling deep understanding of industry characteristics and complex relationships [1] Technological Innovations - The company has developed a reasoning large model (Reasoning LLM) for risk management in micro-enterprises, which features transparent interpretability and high efficiency, with traceable decision processes [2] - The model's accuracy and reliability are enhanced through long-chain reasoning data construction (LongCoT) and reinforcement learning (RL) mechanisms, allowing better understanding and explanation of micro-enterprise business operations [2] - Qifu Technology aims to enhance operational efficiency and risk control precision for financial institutions while promoting deeper understanding and precise services for each micro-enterprise [2]

Core Insights - The 10th Huawei Connect Conference (HUAWEI CONNECT 2025) recently opened in Shanghai, where QiFu Technology's Chief Algorithm Scientist, Fei Haojun, discussed the company's advancements in financial decision-making engines for small and micro enterprises using large model technology [1][3]. Group 1: Industry Challenges - Small and micro finance faces three core challenges: low data transparency leading to a lack of effective decision-making basis, high diversity of enterprise types resulting in disorganized data formats, and traditional risk control models relying heavily on statistics and decision trees, lacking deep causal and logical reasoning capabilities [3]. Group 2: Technological Advancements - QiFu Technology is upgrading three core capabilities: perception, cognition, and logical decision-making to build a new generation of intelligent decision engines [3][4]. - In the perception layer, the company has developed a financial-specific multimodal large model (MLLM) that accurately analyzes vast amounts of non-standard documents like bank statements and income certificates, showing significant advantages in accuracy and recall compared to mainstream open-source models [3][4]. - In the cognition layer, QiFu has created three knowledge graphs covering 33 million enterprise entities, enabling deep understanding of industry characteristics and complex relationships by linking macroeconomic data with business and legal information [3][4]. Group 3: Decision-Making Innovations - The company has developed a reasoning large model (Reasoning LLM) for risk management in small and micro enterprises, which features full transparency and traceability in its decision-making process, supported by long-chain reasoning data construction and reinforcement learning mechanisms [4]. - This "perception-cognition-decision" integrated technology architecture aims to significantly enhance operational efficiency and risk control accuracy for financial institutions while providing deep understanding and precise services for each small and micro enterprise [4][5]. Group 4: Future Directions - QiFu Technology plans to continue advancing technology openness and ecosystem collaboration, aiming to extend its capabilities to more financial institutions and promote smarter, more inclusive, and sustainable financial services for small and micro enterprises, positioning Chinese solutions for global service [5].