The explosive popularity of ChatGPT has ignited global enthusiasm for AI, leading to a surge of investment in the field. Various large models are emerging, with technology evolving rapidly. In just two years, growth has surpassed any previous period. However, as the initial excitement fades, the industry is beginning to consider the practical application of AI technology rationally.

Agree Technology, a banking IT solution provider focused on fintech innovation, aims to serve as a connector between financial institutions and their clients. Over the past 20 years, the company has accumulated both technology and products and deep insights into banking operations and services. Agree Technology clearly recognises its core advantages and industry positioning in today’s AI-driven landscape. It leverages favourable internal and external conditions, integrates its technological and business assets, cultivates new technical talent, and continuously expands its ecosystem of industry partners. This approach enhances the adaptability of its products and technologies, building a unique competitive edge that drives improvements in banking technology and services.

Continuous Exploration of Industry Application Scenarios

Long before the surge in large models, Agree Technology had already begun researching AI applications in various aspects of its ‘Cloud Teller’ solution, including ASR, NLP, and TTS. This forward-looking strategy laid a solid foundation for embracing the era of large models. The company’s unique position in the industry also provides more exploration opportunities for AI applications related to human-computer interaction. These explorations validate our technological capabilities and set goals and directions for enhancing our R&D levels.

After the rise of large models, the R&D team has focused on the practical application of these technologies, concentrating on project construction scenarios to enhance efficiency and quality throughout the project lifecycle. In business processing scenarios, AI is utilised to automate intelligent workflows, reducing or accelerating manual processes, improving response speed and customer satisfaction, and lowering risks. In operational management scenarios, AI assists in decision-making, optimising resource allocation and operational efficiency. The company has identified several AI application types with significant transformative potential, which could reshape competitive dynamics in the industry and create new application paradigms.

Accelerating Technological Integration

Despite the rapid development of AI and large model technologies, recent advancements lack significant breakthroughs compared to the emergence of ChatGPT. With the introduction of the ‘200B problem,’ there is increasing industry focus on AI’s practical application and value extraction. Agree Technology’s ecological niche aligns well with this trend, particularly in banking channel applications. The company aims to leverage its ecological advantages to achieve value innovation through the practical application of AI.

Agree Technology’s research and exploration are always application-oriented, quickly integrating results into existing solutions as value-added modules to enhance product functionality and competitiveness, solve legacy issues, and improve user experience rather than merely exploring technology for its own sake. Additionally, some results will be transformed into dedicated application products for specific scenarios, forming independently sellable products and supporting services. This strategy allows for the rapid application of research outcomes in real-world scenarios. Based on this approach, the company has achieved significant results in the practical business application of AI. On the development side, Agree Technology has created various resource tools for assisted counter transactions, including demand analysis, design schemes, interfaces, interaction logic, print templates, and interface simulation data. On the business side, intelligent operational management helps decision-makers analyse data and formulate strategies more quickly. In contrast, intelligent document Q&A promotes faster access to necessary information, enhancing the efficiency and accuracy of information retrieval.

Given the current enthusiasm for AI, we recognise that widespread industry investment in AI may pose challenges. Therefore, we will remain clear-headed and pragmatically set development goals, ensuring each step is centred around solid value creation. Based on the characteristics of our industry, we will focus on high-value application scenarios, employing a strategy that combines large and small models, utilising a flexible multi-agent architecture to optimise solutions, enhance technical efficiency, reduce overall costs for clients, and improve business flexibility and innovation capabilities, thereby promoting the widespread application of AI technology.