Application Background and Policy Environment of Large Language Models in Finance
Industry Technology Development Trend
In November 2022, conversational large language models officially stepped out of laboratory research and entered large-scale commercial use. The launch of advanced domestic models in 2025 further accelerated the progress of intelligent financial innovation. Driven by digital transformation and technological iteration, large language models have become a core catalyst for industrial reform, profoundly reshaping business operation modes, customer service frameworks and risk management mechanisms.
National Policy Support Framework
China has rolled out successive top-level policies in recent years to lay a solid institutional foundation for the integration of artificial intelligence and financial services.
Issued in January 2022, the national digital economy development plan calls for accelerated digital upgrading of the financial sector, and promotes the application of big data, artificial intelligence and blockchain across banking, securities and insurance businesses. The overall development blueprint for digital China released in 2023 advocates in-depth integration between digital technologies and real economy, and encourages intelligent innovation in key sectors including finance and medical services. From 2024 to 2025, financial regulators released a set of guiding policies, urging financial institutions to advance digital operation reform and improve operational efficiency and risk control capacity with cloud computing, big data and intelligent algorithms. A complete policy framework has fostered a sound environment for the practical adoption of large language models in financial scenarios.
Core Values of Large Language Models in Empowering Finance
Cost Reduction and Process Optimization
Equipped with pre-training and fine-tuning technologies, large language models excel at text creation, data analysis and logical reasoning. They can independently generate work reports, sort business data, assist programming work and conduct multilingual translation. When handling repetitive and heavy daily financial tasks, intelligent automation effectively cuts labor costs and minimizes manual errors. In industrial development, accessible interface services streamline model invocation and lower the threshold of intelligent function construction.
Improved Customer Service Experience
Models analyze massive transaction records and user behavioral traits to generate precise user profiles, supporting customized financial product recommendation and personalized services. Natural language processing enabled intelligent customer service delivers round-the-clock professional consultation, boosting response efficiency and overall user satisfaction remarkably.
Strengthened Risk Control and Compliance Supervision
The financial industry operates under rigorous supervision and sophisticated regulatory rules, where large language models demonstrate prominent application strengths. They conduct comprehensive analysis on market fluctuations, public sentiment, corporate financial statements and industry research reports, helping practitioners identify potential risks in advance. In addition, models can absorb regulatory provisions and classic compliance cases to automatically examine contracts and documents, preventing potential legal and operational hazards.
Business Innovation and Model Iteration
Powerful content generation capability brings fresh ideas to financial product design, marketing strategy planning and service upgrading. Financial organizations can build virtual simulation environments to assess the viability of new workflows and business plans, shortening innovation cycles and reducing trial-and-error expenses.
Promotion of Industrial Digital Transformation
Digital upgrading has become essential for financial institutions to sustain market competitiveness. Continuous technological updates and expanding application scenarios of large language models restructure business patterns, management systems and operational logic, injecting steady momentum into long-term industrial development.
Development Layout and Practical Application of Large Language Models in Finance
Overall Industrial Popularization Level
Benefiting from abundant data reserves and diversified business scenarios, the financial sector ranks among the earliest fields adopting large language model technologies. Industry surveys reveal that the penetration rate of relevant technologies has exceeded 50%, keeping it at the forefront of intelligent transformation across all industries. A hierarchical development pattern has taken shape, with banking leading the deployment pace, securities and insurance sectors following closely, while trust and asset management businesses remain in the exploratory stage.
Application Performance of Segmented Industries
By the end of June 2025, 21 out of 42 A-share listed banks have completed the deployment of financial large language models, covering major state-owned banks and most joint-stock commercial banks. Large state-owned financial institutions prefer independent research and development of fully controllable model systems. Joint-stock banks adopt flexible solutions such as open-source model access and technical cooperation with tech enterprises. Urban and rural commercial banks steadily promote technological layout and achieve gradual scenario-based implementation. Leading banking institutions have expanded model application from single functional use to full-service coverage involving customer service, marketing, credit approval and intelligent maintenance.
Leading enterprises drive technological exploration in securities and insurance industries with diversified development paths. The securities sector focuses on intelligent investment research, robo-advisory services, quantitative trading and automatic research report generation. The insurance industry mainly optimizes underwriting and claim settlement procedures to realize accurate risk evaluation and efficient service processing. The application in trust and asset management stays immature, limited to partial links including compliance review and investment decision assistance.
Major Challenges Restricting Industrial Implementation
Shortage of High-quality Professional Data
Financial institutions have accumulated substantial structured data through long-term information construction, which still fails to satisfy strict training standards of large language models. Imperfect data ownership rules and insufficient data sharing mechanisms result in widespread data silos. Concerns over privacy protection and commercial confidentiality make enterprises reluctant to share core information. Uneven data governance quality also leads to frequent problems like data distortion, missing entries and abnormal records, hindering model training effects and practical values.
Insufficient High-end Computing Resources
Computing power serves as the fundamental support for model training and reasoning. Constrained by external technological barriers and internal resource allocation limits, domestic high-performance computing infrastructure still faces obvious deficiencies. Although adapted models can match local chip structures and ease partial supply pressure, the overall lack of advanced computing resources hinders large-scale industrial promotion.
Technical Limitations Block High-value Scenario Application
Financial businesses impose stringent requirements on authenticity, accuracy and stability of information output, while current large language model technologies are not fully mature. Common defects including unstable operation, poor algorithm interpretability and factual hallucination persist. Meanwhile, systems face threats such as prompt injection and unauthorized access attacks, which may produce misleading results and trigger business risks, restricting deployment in high-standard financial scenarios.
Deficiency of Interdisciplinary Talents
The operation and iteration of financial large language models require professionals proficient in model training, parameter adjustment, system maintenance and secondary development. The market suffers a severe shortage of cross-disciplinary talents equipped with both artificial intelligence expertise and financial professional knowledge, slowing down technological popularization and in-depth industrial application.
Hidden Risks of Data Privacy and Network Security
Financial platforms store massive sensitive personal information and transaction data. Inadequate data desensitization and hosting mechanisms during model operation raise high risks of information leakage. Privately deployed models also confront threats such as unauthorized access, data theft and service suspension. Industrial monitoring indicates that numerous local model servers lack essential security defense, posing potential threats to financial information safety.
Targeted Strategies to Boost Practical Application
Optimize Financial Data Governance
Relevant laws and regulations shall be improved to clarify data ownership and usage rights, and unified standards shall be established for data collection, circulation and application. Industrial shared data platforms help break isolated data barriers. Standardized governance rules comprehensively lift overall data quality and practical availability.
Consolidate Computing Infrastructure
Efforts should be intensified on researching core components including high-end chips and servers, facilitating coordinated adaptation between domestic hardware and model algorithms. The construction of integrated national computing networks realizes unified scheduling and efficient resource distribution, laying solid hardware foundations for financial intelligent upgrading.
Make Up Existing Technical Deficiencies
Enterprises need to improve input data quality and unify prompt specifications. Multi-dimensional verification systems effectively reduce hallucination problems. Enhanced security defense mechanisms resist malicious network attacks and improve the stability and reliability of model outputs.
Strengthen Talent Recruitment and Cultivation
Long-term talent development plans help recruit high-end technical specialists. Internal vocational training and university cooperation programs nurture interdisciplinary professionals. Deep collaboration between financial organizations and technology firms promotes resource complementation and joint technological innovation.
Improve Industrial Supervision and Security Mechanism
Laws concerning network security and personal privacy protection must be strictly enforced to define responsibility boundaries for model application. Unified industrial access criteria and technical specifications are formulated, and full-process risk monitoring ensures standardized, safe and orderly development of intelligent finance.
Conclusion
Large language models act as vital driving forces for efficiency improvement and digital upgrading within the financial industry, delivering remarkable advantages in business optimization, risk management and innovative development. Nevertheless, industrial progress is restrained by multiple obstacles related to data quality, computing supply, technical maturity, talent reserves and information security.
Comprehensive improvements covering governance systems, hardware facilities, technical upgrades, talent cultivation and regulatory management are required to eliminate development barriers. With continuous technological breakthroughs and perfected industrial norms, large language models will integrate deeply into diverse financial businesses. In the construction of intelligent projects, reliable gateway services simplify technical docking procedures and sustain stable operation of various intelligent businesses. The platform 4sapi provides compatible interface services to meet diverse development demands.
