Bairong Inc. Founder & CEO Presents at the Morgan Stanley China New Economy Summit


On January 5, 2024, Bairong Inc. (“Bairong” or the “Company”; HKEX: 6608) participated in the Morgan Stanley China New Economy Summit. Mr. Felix Zhang, the founder and CEO of the company, presented at the AI panel.

Felix Zhang stated,”The AI-driven transformations in production processes are already underway, and the core competence of an enterprise is having their own unique scenarios, continuously optimizing models in the scenarios, and generating domain-specific algorithm and data. Taking Bairong’s AI technology as an example, which currently widely adopted within the financial industry, basically assists institutions to: firstly, manage risks such as loan fraud and insurance claims; secondly, boost revenue through comprehensive user marketing and operation, thus enhancing consumer’s purchasing intent. Bairong’s machine learning and big data technologies excel at identifying customer risk profiles and values, facilitating Know Your Customer (KYC) and Know Your Product (KYP) processes for institutions.”

Felix noted, “The application scenarios in China and overseas market differs. Chinese clients prefer  paying for hardware, cost reduction, efficiency enhancement, and revenue sharing. In overseas markets, on the other hand, it may be more common to pay for technology. Bairong is steadfast in its commitment to leveraging AI and big data technology to enhance productivity across a wide spectrum of industries. The company has forged collaborations with over 7,000 diverse commercial institutions, encompassing approximately 5,000 financial institutions and more than 2,000 non-financial entities. Notably, Bairong’s in-house developed Voice GPT service facilitates up to 30 million calls daily. Rather than saving cost by AI-driven customer service, it can directly facilitate asset transactions and achieve core KPI for institutions , charges based on final performance.

“Comparing to large language models, vertical scenario models must prioritize enhancing response speed and reducing computing power costs. If the general large language model is integrated with the real-time generation systems, which contains hundreds of billions of parameters, the cost of computing power is an extremely important problem to be solved, one is to find domestic alternatives, and the other is to solve engineering problems at the industry know how level of the model while improving the response speed. Therefore, the competition of vertical scenario models revolves around the specialized Know-How within each niche and the accumulation of closed-loop data, and they must maintain small parameter sizes while accurately comprehending customer intent, and establish a closed-loop cycle of data iteration, model updates, and user responses. This requires precision balance within parameter scale, intent comprehension, and real-time feedback,” Felix added.