See the application of artificial intelligence in the financial field from ChatGPT

Author: Yang Tao, deputy director of the National Finance and Development Laboratory

Summary

Since ChatGPT was born, this artificial intelligence dialogue robot has become one of the hottest topics in the world. If we discuss the application status of artificial intelligence in the financial field from the perspective of ChatGPT, objectively speaking, artificial intelligence has a higher degree of application in organizational operations, service capabilities, and risk management. Due to technical and institutional factors, it is not enough to solve other financial needs. Insufficient. The article pointed out that although ChatGPT has further highlighted the application capabilities of artificial intelligence, it still faces many challenges for the financial industry, making it unable to bring major changes to the financial industry for a long time.

ChatGPT, a dialogue robot developed by OpenAI, an American artificial intelligence research laboratory, has attracted widespread attention from all walks of life at home and abroad, and set off a round of artificial intelligence boom. At the same time, the digital transformation of the financial industry has become the general trend of all countries, and it is also an important reform direction promoted by the regulatory authorities in my country. Therefore, starting from ChatGPT and in-depth analysis of the status, opportunities and challenges of the application of artificial intelligence in the financial field will help to more accurately realize the high-quality development of science and technology in finance.

01 The status quo of artificial intelligence development and the status of ChatGPT

From a macro perspective, artificial intelligence is regarded as the core driving force of the digital economy, whether it is the top-level design of the "14th Five-Year Plan" and the digital economy development plan, or the new version of the financial technology development plan and digital transformation guidance in the financial sector. Key industries and digital pedestals. The rapid development of the digital economy has created a good economic and technical environment for artificial intelligence; at the same time, artificial intelligence, as a key new infrastructure, has also provided new momentum for the development of my country's digital economy. On the whole, the state-of-the-art open and shared infrastructure, focused and implemented tool processes, and diverse and broad application scenarios provide a good application environment and market space for the vigorous development of artificial intelligence. **

From the perspective of technological development trends, the ultra-large-scale pre-training model is undoubtedly the focus and hot spot of the current development of artificial intelligence technology. In the past two years, there has been a major explosion and an "arms race". Overall, the large model shows the development trend of multi-modality, multi-technology, multi-capability and multi-application. It has shown good application effects in the ideal laboratory environment and the real environment of vertical industries. In the future, large and small models will be formed. An intelligent system that develops collaboratively between the cloud and the edge.

At the same time, artificial intelligence has also brought huge impact and challenges to the existing ethical norms and social governance. Therefore, how to realize the effective governance of artificial intelligence has become the focus of attention from all walks of life at home and abroad in recent years. It can be seen that artificial intelligence governance at home and abroad has made breakthrough progress, and has entered the stage of establishing rules and regulations and implementing it from the conceptual level, and the development of credible AI has become the core content.

It should be said that artificial intelligence has become the most important "catalyst" of technological innovation, and natural language processing (NLP) related to ChatGPT is considered to be the "pearl" in the crown of artificial intelligence. We have seen that the history of the development of artificial intelligence is actually a history of continuous improvement of model dimensions, from human experts writing rules, to machines writing a small number of rules, to machines writing a large number of rules, and finally to transfer learning large models. In this process, ChatGPT uses text learning methods to expand the field. GPT-3 has 500 billion words and 175 billion parameters. Finally, with the support of massive information, it has obtained a comprehensive improvement in functions, but there are also problems with credible content and data. The challenges of safety and high landing costs.

02 From the perspective of financial demand to see the application opportunities of artificial intelligence

With the in-depth advancement of the digital economy and the construction of a digital society, a large amount of data has been generated, providing a broad "soil" for the modeling, training and application of artificial intelligence. In particular, the accumulation of large-scale and high-quality data in the financial field, as well as multi-dimensional and diversified application scenarios, provides a good opportunity for the vigorous development of artificial intelligence applications. Through the in-depth integration of artificial intelligence and financial customer service, product innovation, operation management, risk prevention and control and other business scenarios, the whole process of financial services will be reshaped and intelligently empowered, and financial product innovation, process reengineering, channel integration and service will be promoted. Upgrade and expand the breadth and depth of financial services, becoming an important source and driving force for financial digital transformation.

In the final analysis, the application value of **artificial intelligence lies in solving the problems existing in the financial field, which needs to be analyzed from the perspective of financial needs. Specifically, from the perspective of the mesocosmic and microcosmic levels of the financial industry, the difficulties faced are strategic issues. ** Facing the increasingly complex economic and financial situation, the formulation of strategies for financial institutions has become particularly important. This is not only a "top project" of the institution, but also requires an effective combination of vision, logic, and experience, as well as timely and effective dynamic management. optimization. The application of artificial intelligence in strategy formulation is perception, reasoning, and decision-making. It is naturally possible to combine with comprehensive or special strategy formulation of financial institutions and perform dynamic random optimization.

**The second is a structural problem. **Although the comprehensive strength of my country's financial industry continues to increase, there are still many structural contradictions of unbalanced and insufficient development, which also put forward requirements for artificial intelligence to "make up for shortcomings". For example, whether the application of artificial intelligence in the field of wealth management can bring changes to the imbalance of household asset structure and financial asset layout will directly affect the major goal of finance to help common prosperity.

** The third is the issue of factors of production. **Sustainable development and digital transformation of financial institutions need to consider the economy, scale, and efficiency of factor inputs, the core of which is data and people. On the one hand, data has become an important factor of production and a basic national strategic resource. How the financial industry can improve the full lifecycle activities of data "acquisition, storage, calculation, management, and use" and promote the transformation of data elements into data assets is an urgent challenge at present, and the combination of artificial intelligence and big data will stimulate more vitality. On the other hand, fintech talents are also a scarce resource.

Artificial intelligence can become a "smart assistant" to enhance the capabilities of employees, or it can complement team capabilities by building "digital humans".

** The fourth is the issue of organization and operation. **The digital transformation of the financial industry is inseparable from the guarantee of organizational structure and operational capabilities. In this process, artificial intelligence can be fully utilized to create an automated and intelligent operating model, continuously optimize operating processes, innovate operating models, improve operational service quality, and reduce Operating costs, thereby supporting comprehensive and intelligent financial services.

** The fifth is the issue of service capacity. **The service capabilities of financial institutions are reflected in diversified products, sufficient market analysis capabilities, marketing and channel capabilities, customer maintenance and value-added service capabilities, etc. Especially in terms of customized smart product design, precise marketing of customer holographic portrait services, and consistency of online and offline experience, fruitful explorations have been made.

**The sixth is the issue of risk management. **Currently, the macro and micro risks faced by the financial industry are more complex. If artificial intelligence can be effectively used, an intelligent risk control model can be established on the basis of integrating and analyzing big data, which will become an effective way to identify, monitor and control risks. On the one hand, it constructs customer, business and risk views to dynamically and comprehensively reflect the overall picture of risks; on the other hand, it optimizes intelligent credit risk assessment and realizes the transformation of risk control to numerical control and intelligent control.

** The seventh is the issue of service effectiveness. ** Whether the application of artificial intelligence in the financial industry is efficient, one is from the perspective of financial institutions themselves, and the other is from the perspective of service entities. On the one hand, during the rapid development of the financial industry in recent years, information technology has had a very profound impact on the improvement and transformation of the total factor productivity of the financial industry. One of the manifestations of the use value of artificial intelligence is whether it can further improve the operating efficiency of financial institutions and optimize financial indicators. On the other hand, the financial industry still has many responsibilities in helping inclusive, green, technology, and common prosperity. The value of artificial intelligence applications to improve their functions also needs to be considered.

The eighth is the ecological issue of cooperation. **From open banking to open finance has become the mainstream of global innovation, and financial institutions need to share data, algorithms, transactions, processes and other business functions with the business ecosystem to provide ecosystem customers, employees, third-party developers, financial institutions Technology companies, suppliers and other partners provide services to create a digital financial ecosystem characterized by "intelligence, openness, sharing, agility and integration". With the blessing of artificial intelligence and big data, it may help to further improve the external ecology of financial institutions.

Objectively speaking, artificial intelligence is more widely used in organizational operations, service capabilities, and risk management. Due to technical and institutional factors, it is still insufficient in solving other financial needs.

03 Challenges in the application of artificial intelligence in the financial industry

ChatGPT** further highlights the application capabilities of artificial intelligence, but for the financial industry, it still faces many challenges, making it impossible to bring major changes to the financial industry for a long time. **

** The first is data governance. **The starting point of the digital transformation of the financial industry is to do a good job in data governance. It is necessary to truly improve the data governance system, enhance data management capabilities, strengthen data quality control, and improve data application capabilities. The application of artificial intelligence is also inseparable from high-quality massive data, but the data governance of financial institutions is generally in its infancy, and low-quality data, data islands, and scattered data are common, and it is difficult to provide sufficient data element support for artificial intelligence.

**The second is the standardization of the scene. **Although the financial application of artificial intelligence embodies the characteristics of personalization and "thousands of people and thousands of faces", in the long run, in the process of integration of finance and technology, the real vitality is the standardized and general-purpose financial technology innovation scene, while Differentiated cooperation not based on the traditional outsourcing model is also one of the constraints of existing artificial intelligence financial applications.

** The third is the high cost threshold of technology and solutions. **Technical applications and solution settings of artificial intelligence in financial activities usually have high deployment costs, making it difficult to meet the needs of small and medium-sized financial institutions. According to research estimates by Guosheng Securities, the cost of GPT-3 training is about 1.4 million U.S. dollars, and for some larger LLM (Large Language Model), the training cost is between 2 million U.S. dollars and 12 million U.S. dollars.

** The fourth is transparency and inexplicability. **The so-called interpretability refers to the need to obtain sufficient and understandable information in the process of cognition or decision-making of an action, so as to help decision-making. In machine learning, there is usually an unobservable space called a "black box" between the input data and the output answer. Only by developing interpretable and trustworthy AI financial applications can user trust, model auditability and risk reduction be achieved.

** The fifth is internal coordination within the organization. **As far as financial institutions apply artificial intelligence and other cutting-edge technologies, it is usually difficult to form an effective "incentive compatibility" mechanism to promote internal stakeholders to reach a consensus to reflect the value of technological innovation with maximum efficiency. In this regard, how to optimize the organization and coordination model through rule design while optimizing the iteration of the technical solution itself is also an unavoidable challenge for artificial intelligence.

** The sixth is responsibility sharing. **The product design and business operation of financial institutions have certain particularities, and there are also various complex risks. Therefore, based on the logic of risk control and financial consumer protection, any financial activity needs a clear responsibility sharing mechanism. After the introduction of artificial intelligence, the balance of rights and responsibilities in the original business process of financial institutions may cause some new ambiguities, which urgently need to be further explored from the aspects of institutional rules, business practices, technology and business, and the relationship between models and people.

**The seventh is compliance and ethics. **With the rapid development of financial technology, the supervision of various countries is advancing with the times. Facing the dynamic evolution of regulatory principles and models, the financial application of artificial intelligence has more prominent compliance pressure. At the same time, the ethical challenges of financial technology such as algorithm discrimination, big data killing, and information leakage have also brought "shadows" to the application of artificial intelligence. It is still necessary to explore how to use "responsible" technological innovation to create "warm" financial services .

In short, the prospect of artificial intelligence-driven digital transformation of the financial industry has begun, but it has not been smooth sailing, and there are still many major challenges, which urgently need self-optimization and continuous "breakthrough".