优质客群流失、高风险客群难承，中小银行陷两难困局

Core Viewpoint - The Chinese lending assistance industry is undergoing a significant transformation from a focus on scale to a focus on quality due to the implementation of new regulations, which have rendered the previous "guarantee" model unsustainable [2][4]. Group 1: Industry Background - The lending assistance industry in China has developed over more than a decade and has become an integral part of the financial system [2]. - The "guarantee" model allowed small lending platforms to leverage funds by assuming responsibility for bad debts, thus gaining banks' trust [3]. - This model has been criticized for transferring credit risk from banks to lending platforms, increasing systemic risk in the industry [4]. Group 2: Regulatory Changes - New regulations have cut off the profit chain that supported the "guarantee" model, with a focus on controlling interest rates above 20% [4]. - The downward trend in interest rates is squeezing the profit margins of lending platforms, making it difficult for them to cover bad debt compensation [4]. - Regulatory bodies have emphasized the need for banks to strengthen their risk control responsibilities and not outsource critical risk management functions [6][8]. Group 3: Market Dynamics - The cost of customer acquisition for lending platforms has significantly increased, with costs rising from a few cents to around 1000 yuan per customer [5]. - Smaller banks are struggling to compete for quality clients and are increasingly forced to handle high-risk clients due to their lack of core risk control capabilities [5]. - The new regulations are pushing the industry towards a consolidation phase, with leading lending institutions becoming preferred partners for banks [7]. Group 4: Future Directions - The industry is shifting towards a model that prioritizes quality over scale, with some smaller platforms exploring transitions to technology service providers [7]. - Banks are required to rebuild their independent risk control capabilities, focusing on developing localized risk models and integrating various data sources to enhance risk assessment [8].