What impact will ChatGPT have on the financial industry?

Fintech has been widely used and rapidly developed in recent years, which has profoundly changed the business model of finance, the inner logic of finance and the behavior of the financial workplace. And big data, blockchain, artificial intelligence, Internet of Things, etc. are originally the main components of FinTech. With the improvement of the level of artificial intelligence technology, FinTech will certainly have a broader and deeper impact on the financial industry.

First, customer service and digital marketing of financial products. Customer service is the application of artificial intelligence can most quickly play a role and effect of the scene. So far, various trading platforms have widely adopted robot customer service. But on the whole, the effect is not ideal. The reason for this is that the robot customer service lacks the ability to sense the thousands of different linguistic expressions of customers, lacks empathy for customer needs, and does not have a wide knowledge base. Judging from ChatGPT's performance, high-level AI is likely to be more experienced, more knowledgeable, and more responsive than human customer service. It can be expected that in the near future, manual customer service will be completely replaced by intelligent robots, the number of customer service jobs will be drastically reduced, and the huge customer service workplaces will no longer exist, thus largely reducing the labor costs and management costs of financial institutions.

With the development of financial technology, digital marketing has played a big role in mining a large number of "long tail" and "sleeping" customers of financial institutions. Artificial intelligence deepens the understanding and dialogue function in the marketing process, improves the recognition accuracy, and allows high-quality one-to-one communication with respondents, effectively solving the problems of high cost of manual follow-up, difficulty in managing manual seats, and difficulty in real-time data monitoring. The personalized service that has been highly called for in recent years has put forward extremely high requirements for the professional ability of marketers, the accuracy of demand recognition and the flexibility of response, and the wide application of artificial intelligence helps to rapidly improve the product marketing ability of financial institutions.

Second, financial risk management. Preventing risks is the vocation and core responsibility of financial institutions. As an intermediary of funds, financial institutions are exposed to various types of risks, such as credit risk, market risk, management risk, liquidity risk, legal and compliance risk, etc. In the face of risks, financial institutions should first establish and improve their risk management system. In the face of risks, financial institutions must first establish a sound internal control and risk management system, and set up a sound risk management framework to classify, evaluate and manage various types of risks. In this process, artificial intelligence can play a full role, including monitoring the implementation of the system, responding quickly to the highly volatile market, and scientifically assessing the type and extent of risks. Second, employees' awareness of risk and their ability to implement systems are key to risk management in financial institutions, and risk managers in particular should have extensive experience and theoretical knowledge of risk management. The introduction of artificial intelligence in the process of providing systematic training and education for employees by financial institutions can effectively improve the training efficiency and accurately detect the risk management capability and level of personnel in key positions. Again, risk information disclosure and disclosure is the responsibility and obligation of financial institutions to the public. The disclosure of information involves a large amount of data and information, and it is difficult to process this information scientifically, accurately and quickly by human resources alone; artificial intelligence can also greatly improve efficiency in this field.

Third, product pricing. The essence of financial product pricing is risk assessment, which requires risk assessment of customers based on their creditworthiness, repayment ability, financial status and other factors, and the development of different risk premiums or discounts to avoid possible losses due to default by customers. Due to the diversity and complexity of financial products, a lot of knowledge and skills in mathematics, statistics and economics need to be applied. Take insurance actuarial as an example, a reasonable insurance actuarial can not only protect the insurance company's own interests, but also help protect the rights and interests of customers. Factors involved in insurance actuarial include at least: the risk of insurance products (type of underwritten risk, risk level, insurance liability, insurance term, insurance amount, deductible, etc.), historical data and statistical analysis (average life expectancy, accident probability, weather variation, etc.), insurance product risk (predicted loss, probability distribution, time value, etc.), policy regulations and regulatory requirements, economic environment (inflation, interest rate, etc.) and market competition, insurer's underwriting capacity (asset-liability position and matching, investment portfolio, earnings budget, reserves, solvency), etc.

Risk modeling for financial institutions is a very complex system that requires a combination of knowledge and skills in multiple areas such as risk assessment, data collection, mathematical modeling, model validation and risk management. In fact, in the process of establishing and applying and testing risk models, financial institutions have already applied a lot of financial technology, and the addition of high-level artificial intelligence will further enhance the scientific nature of these models, and it is possible for artificial intelligence to replace actuaries to a certain extent.

Fourth, the insurance survey claims. The biggest risk insurance companies face when a policy is insured is fraud. Billions of dollars in fraudulent claims occur every year. To reduce such risk, insurance companies must conduct the necessary investigation and review of claims applications to carefully screen the authenticity and extent of losses and provide a basis for claims decisions. In addition, due to the large volume, multi-disciplinary and complex nature of the policies, the investigation of claims is often time-consuming and costly. Artificial intelligence can greatly simplify this process, eliminate human error, and enhance the science and speed of claims processing.

Fifth, investment advisors. Fintech has started to be widely used in the field of securities investment, including quantitative investment, providing customers with personalized investment advice and recommendations, and optimizing customers' investment portfolios under the premise of ensuring risk control and maximizing returns. However, in the field of PE and VC investment, AI still mainly appears as an object to be invested rather than as an investment decision tool. In the future investment advisory scenario, AI should be able to use its powerful database, knowledge base and analytical capabilities to help the private equity industry make more scientific investment decisions and improve portfolio returns and risk control.

Sixth, family asset management. Compared to the investment advisory industry, which mainly serves institutional and high net worth clients, family asset management is still largely a gap in China. This is due to the fact that Chinese families traditionally find it difficult to accept fee-based services, but also due to the existence of barriers between different industries and the lack of financial institutions' ability to provide customized services for families throughout their life cycle. For example, bank account managers can only recommend a small number of products such as funds and wealth management outside of the traditional deposit and loan business, and customers who want to buy securities or insurance must have direct contact with financial institutions that offer the corresponding products. The data processing capability of artificial intelligence will help financial institutions as well as third-party service providers to develop the huge market of household asset management, thus further enhancing the efficiency of financial services.

Based on the tremendous power of AI in improving response speed and work efficiency, financial institutions will definitely use AI more in the future and use it to give rise to more financial service scenarios and new profit models. It can be expected that the development of financial technology will continue to develop in the direction of digitalization, intelligence, personalization and cross-border, thus further deepening the differentiation of financial services and making different categories of financial services more integrated and innovative.