The financial industry finds itself at a pivotal moment, grappling with a host of challenges that necessitate a fundamental shift in operational paradigms. Traditional business models are under immense pressure as they struggle to keep pace with rapidly changing customer needs and preferences. Recently, Zeng Gang, Deputy Director of the National Financial and Development Laboratory, emphasized the urgent need for large-scale artificial intelligence (AI) models during a forum dedicated to innovations in financial productivity. His insights underscore a critical turning point for the sector, highlighting the necessity for transformation to remain competitive and relevant.

Zeng identified several pressing factors contributing to the industry's predicament. First and foremost, asset acquisition has become increasingly difficult, with the discovery of new clientele proving more challenging than in previous years. Conventional service methodologies that once thrived in reaching potential customers are losing their effectiveness, signaling a need for a new approach. Additionally, the financial landscape is witnessing shifts in asset quality, compounded by the adjustments in oversupplied industries that heighten the stakes for effective risk management. Adding to this complexity are narrowing interest margins and challenges in capital replenishment. In light of these factors, it becomes imperative for financial institutions to leverage advanced AI models to innovate and enhance existing frameworks.

Central to this transformation is the concept of the "financial big model," a sophisticated application of AI specifically designed for the financial sector. This approach goes beyond merely expanding the reach of financial services; it aims to deepen the quality and effectiveness of those services. Lu Yong, CTO of Lexin, elaborated on this concept, explaining how these models can autonomously analyze diverse data streams. By doing so, they uncover critical insights regarding customers, including their industry affiliations and repayment tendencies. This capability allows for the creation of distinct, personalized customer profiles, which can drive precision-targeted marketing strategies. The implementation of big models has already gained traction in essential business processes such as telemarketing, customer service, and collections. Furthermore, these technologies have proven invaluable in back-office operations like code development support, creative design generation, and data analytics, significantly boosting overall operational efficiency.

The microfinance sector, in particular, has long faced challenges rooted in information asymmetry and difficulties in risk management. Traditional methods of constructing credit profiles often fail to capture the nuanced realities of micro and small enterprises. Enter big model technology, which offers a fresh perspective on these enduring issues. Yang Jian, an expert in big models at Qifu Technology, discussed how the integration of large model technology enables comprehensive data mining and analysis. In the realm of micro-identity verification, big models can synthesize user behavior across various platforms alongside key quality and demand variables. This synthesis allows for accurate classification of individuals as business owners or proprietors. Beyond simple identification, Qifu Technology delves deeper into users' financial obligations and their interactions with competing financial products. By analyzing borrowing patterns from institutions such as consumer finance services, small loan providers, and banks, Qifu provides critical insights for financial entities striving to manage risk effectively. Thanks to these advancements, Qifu has achieved an impressive industry information coverage rate of 94.5%.

Despite the promising advancements represented by big models, the path toward comprehensive implementation is fraught with obstacles. Jiang Ning, CTO of China Consumer Finance Co., highlighted several challenges that remain, particularly in the realms of group intelligence, safety, personalization, privacy protections, key task management, and adaptive standards. He emphasized the critical need to cultivate four pivotal technological capabilities: controlled model security, combinatorial AI development, continuous learning protocols, and robust platform service capabilities. Jiang reiterated the importance of establishing a solid technical framework centered around these competencies, which would serve as the foundation for promoting high-quality growth in digital finance.

Meanwhile, Gong Xiaojun, CIO of DBS Bank China, offered insights into the promising opportunities that domestic big models present, particularly in three key areas: data accumulation, experiential learning, and enhanced computational power. He articulated that optimizing big models requires substantial human resources and diverse scenarios for training these systems, noting that a wealth of foundational data already exists to facilitate this endeavor. Furthermore, Gong identified an opportunity to learn from the challenges faced in early international applications of these models, which could inform better practices moving forward. He also stressed the importance of investing in breakthroughs in computational power through cloud computing solutions to bridge current gaps.

In summary, the financial sector stands on the brink of a significant transformation catalyzed by the advent of large-scale AI models. The pervasive challenges surrounding asset acquisition, quality management, and capital constraints compel a reimagining of how financial services are delivered. As industry leaders harness the potential of these technologies, they are poised to drive unprecedented innovation that promises to redefine value creation in finance. Embracing these changes will not only enhance operational efficiency but also foster a more responsive and personalized banking experience for customers.

The potential impact of these advancements extends beyond mere technological improvements. As financial institutions integrate large models into their operations, they can better respond to the needs of their clients, adapting services to fit individual circumstances and preferences. This shift towards personalization is vital in an era where consumers increasingly expect tailored solutions that align with their specific needs.

Moreover, the implementation of big models can significantly contribute to financial inclusion, particularly in underserved markets. By leveraging advanced data analytics, financial institutions can gain insights into the creditworthiness of individuals and small businesses previously deemed too risky to lend to. This capability opens the door to new opportunities for economic participation, empowering individuals to access the financial resources necessary to grow their businesses and improve their livelihoods.

Collaboration among stakeholders will be essential as the financial industry navigates this transformative landscape. Financial institutions, technology providers, and regulatory bodies must work together to create a conducive environment for innovation. Establishing clear guidelines and standards for the deployment of AI models can help mitigate risks associated with data privacy and security, ultimately fostering greater trust among consumers.

In conclusion, the evolution of the financial industry is being shaped by the integration of large-scale AI models, which promise to enhance operational efficiency, improve risk management, and deliver more personalized customer experiences. By embracing these technologies, the sector can address its current challenges and position itself for future success in an increasingly competitive landscape. As this transformation unfolds, the financial industry stands to benefit from a renewed focus on innovation that prioritizes customer needs and drives sustainable growth. In doing so, it can redefine itself for a new era, ultimately emerging stronger and more resilient in the face of evolving market dynamics.