Group 1 - The core issue facing the banking industry is the "scarcity of quality assets" in the inclusive credit business, which can be addressed through a "data + scenario" collaborative model [1] - Current inclusive credit operations primarily rely on the "bank-tax interaction" mechanism, which reflects the results of business operations rather than real-time dynamics; invoice data is emerging as a crucial supplement [1] - Case studies presented at the conference illustrate that transaction data is more indicative of a company's true creditworthiness compared to traditional tax and financial reports [1] Group 2 - High-quality data is essential for the sustainable development of inclusive finance, emphasizing the need to not only possess data but also to understand it within specific industrial contexts [2] - The newly launched "Tax Invoice Anti-Fraud AI Model 2.0" by the company integrates AI and GraphRAG technology to identify fraudulent activities, enhancing risk management in loan approval and monitoring [2] - The challenge of distinguishing quality small and medium enterprises (SMEs) is prevalent among banks, with the company suggesting that quality SMEs are often embedded within supply chains rather than being concentrated among leading firms [3] Group 3 - The company has developed a service model called "one industry, one solution" in collaboration with major banks, embedding credit services directly into real transaction chains [3] - Utilizing transaction graphs and privacy computing technology, the company can analyze the authenticity and continuity of invoice data, creating a whitelist of quality enterprises within the industry ecosystem [3] - Within three weeks of launching this service model, the credit scale exceeded 60 million yuan, demonstrating the feasibility of this scenario-based service approach [3]