Morgan Stanley and other institutions have introduced ChatGPT. What are the applications of generative AI in the financial field?
What are the applications of generative AI in the financial field? What are the advantages? What challenges do you face? understand
See the application of generative AI in the financial field from several business scenarios and practical cases
ChatGPT launched a privacy function, the European Union accelerated the formulation of new regulations, and generative AI has a bright future in the financial field
Attitudes towards the application of generative AI in the financial sector are now divided into two factions.
Big banks such as Bank of America, Citigroup, and Goldman Sachs quickly restricted employees from using ChatGPT in late February because of data breaches and other issues.
However, other financial companies are still actively exploring and trying to apply generative AI.
Morgan Stanley, for example, is using an OpenAI-powered chatbot to assist financial advisors as a knowledge resource that leverages the firm’s internal research and data repositories.
Hedge fund Citadel is negotiating an enterprise-level ChatGPT license that will be used for software development and information analysis.
Expense management platform Brex is also working with OpenAI to roll out chat-based spending insights and benchmarks for clients. Bloomberg is developing BloombergGPT, a large-scale finance-specific language model for sentiment analysis, news classification, and other financial tasks.
In China, as early as February, China Merchants Bank released a publicity draft on the bank’s family credit card with the participation of ChatGPT on its official microblog. Bank of Jiangsu has jointly applied ChatGPT and Codex technology to analyze the operation of the bank’s information system. make relevant suggestions.
Although ChatGPT has experienced data leakage, Samsung has also become a typical example of commercial data leakage caused by the use of ChatGPT. However, financial businesses are still cautious and optimistic about the attitude and application of generative AI technology.
And for most enterprises, through private deployment, security protection, synthetic data and stable automation, some factors can still be controlled.
Especially after ChatGPT launched a new privacy feature a few days ago, as long as the chat history is turned off, the user's data will not be used to train and improve OpenAI's artificial intelligence (AI) model. This function has effectively curbed the data leakage of organizations using ChatGPT.
There is another piece of information worth thinking about recently, that is, Nvidia has launched "guardrail" software such as NeMo Guardrails to prevent random output and nonsense of generative AI. This may mean that in order to better serve customers and expand market share, more companies will launch software applications that limit and optimize generative AI.
The European Union is accelerating the formulation of new rules for generative AI, and will plan to set up an "AI production" label, which will give more regulation to generative AI.
China has also drafted the "Generative Artificial Intelligence Service Management Measures (Draft for Comment)" and publicly solicited opinions from the public. The "Measures" clarify that the state supports the independent innovation, promotion and application, and international cooperation of basic technologies such as artificial intelligence algorithms and frameworks. Therefore, formulating management measures is just to better develop the technology in the future, not to limit the technology.
The initiatives of various organizations are pushing generative AI to a constrained positive track, which will be very conducive to the great development of generative AI in the financial field.
Having said so much about the development trend of generative AI in the financial field, what are the applications of generative AI in the financial field? What are the advantages? What challenges are you facing?
In this article, Wang Jiwei Channel will talk to you about these.
Applications of Generative AI in Finance
Generative AI is a kind of artificial intelligence technology, which uses methods such as deep learning to learn rules from a large amount of data, and according to given conditions or goals, automatically generates technology that meets the requirements of text, images, audio and other content.
Generative AI is unique compared to other AI techniques in its ability to create new content. For example, the Generative Pretrained Transformer (GPT) is a large-scale natural language technique that uses deep learning to generate human-like text.
OpenAI's third-generation GPT (GPT-3) has been able to predict the most likely next word in a sentence based on the training it has absorbed, and has been able to write stories, songs and poems, and even computer code.
In view of these advantages, generative AI can be applied to various business scenarios in the financial field. The following are some typical applications.
Robo-advisor: Provide customers with personalized investment advice and portfolio optimization based on their risk preferences, income goals and asset status. Generative AI can use technologies such as big data analysis, deep learning and reinforcement learning to monitor market dynamics in real time, adjust investment strategies, increase returns and reduce risks.
Intelligent investment research: Generative AI technology can provide investors with evaluation and prediction of financial products such as stocks, funds, bonds, as well as investment strategies and suggestions by analyzing massive financial data, news, social media and other information.
For example, Morgan Stanley's AI models can analyze news reports, social media posts and financial statements, among other things, to identify patterns and predict stock prices. AI tools such as ChatGPT can analyze the impact of news headlines on company stock prices, or decipher the potential impact of central bank policy statements on financial markets.
Intelligent risk control: Provide financial institutions with accurate risk assessment and fraud detection by analyzing data such as customer credit history, behavioral characteristics, and social relationships. Generative AI can use technologies such as graph neural networks, adversarial generative networks, and anomaly detection to mine potential risk factors, identify abnormal behaviors, and prevent financial losses.
Insurtech: Generative AI can provide customers with customized insurance products and services based on their needs, preferences and scenarios. Generative AI can use technologies such as conditional generation network, text generation, and image generation to simulate different insurance scenarios, generate suitable insurance plans, and improve customer experience and satisfaction.
The application scenarios of generative AI in the financial field are far more than these. For example, Industrial Bank, Wenxinyiyan's first batch of ecological partners, has carried out the application of artificial intelligence large-scale model technology in financial scenarios such as smart outlets, smart services, smart risk control, smart operations, smart marketing, smart investment research, smart wealth management, and smart customer service. .
With the continuous development and innovation of artificial intelligence technology, generative AI will play a greater role in the financial field and bring more value and opportunities to the financial industry.
Application cases of generative AI in the financial field
Through the previous application scenarios of generative AI in the financial field, you can see that it has a wide range of application values in finance. The role of generative AI in improving the efficiency and experience of financial services, reducing financial risks and costs, and creating new financial products and models.
In order to facilitate everyone's understanding, here are a few more specific application cases.
Case 1: Applied to intelligent customer service
Intelligent customer service refers to the use of generative AI technology to provide financial users with 24-hour online consultation, handling and problem-solving services through voice or text.
Intelligent customer service can be based on large-scale model technologies, such as ChatGPT, etc., combined with professional knowledge and data from the financial industry for interactive training, so as to achieve multiple rounds of complex dialogue, natural language understanding and generation, emotion recognition and adaptation. Smart customer service can be applied to multiple business links such as credit products, wealth management products, and insurance products, greatly improving user satisfaction and conversion rates, and reducing labor costs and risks.
For example, N26, a leading mobile bank in Europe, has deployed the Rasa voice assistant based on generative AI technology in its cloud environment, which can run in five different languages in its mobile and web applications, and can handle lost or stolen credit cards. complex tasks such as reporting. By adjusting the machine learning model, N26 made its own data set achieve the best performance, and achieved 20%-30% of customer service requests for language assistants in a short period of time.
Case 2: Applied to intelligent risk control
Intelligent risk control refers to the use of generative AI technology to provide effective risk warning and prediction for financial institutions through the analysis and modeling of massive data, so as to reduce the financial risk of the whole society. Intelligent risk control can be based on large language model technology, such as LLM, etc., combined with Internet text data, behavior data, and credit reporting data, so as to identify risk indicators in more dimensions and better evaluate the credit of small and micro business owners. risk.
Intelligent risk control can be applied to multiple links such as credit approval, post-loan management, anti-fraud, and anti-money laundering, which can greatly improve the efficiency and accuracy of risk control, and reduce the non-performing rate and loss.
Case 3: Applied to intelligent interaction
Intelligent interaction refers to the use of generative AI technology to provide financial users with a richer and more convenient interactive experience in a multi-modal way.
Intelligent interaction can be based on multi-modal model technology, such as AutoGPT, etc., combined with image, voice, video and other media information to understand and generate, so as to realize cross-media information conversion and presentation. Intelligent interaction can be applied to multiple scenarios such as financial marketing, financial education, and financial entertainment, which can greatly increase user participation and loyalty, and increase user stickiness and income.
Intelligent interaction is not only applied to customers, but also applies to internal financial development business.
For example, the technology team of the Bank of Jiangsu has conducted beneficial explorations in the application of ChatGPT. The technical personnel jointly applied ChatGPT and Codex technology to analyze the operation of the information system in the bank, and automatically analyzed and obtained relevant suggestions.
The code runs in the production environment, and it takes less than 1 hour to complete all the requirements perfectly. The time to write functions has been greatly shortened, and the time spent on communication with manufacturers has been shortened from days to hours.
Advantages and challenges of generative AI in the financial field
After exploration and testing by researchers and related institutions, in the financial field, generative AI tools such as ChatGPT have been widely used, such as analyzing the impact of news on stock prices, interpreting policy statements, and assisting investment decisions.
In general, the advantages of generative AI in the financial field are as follows:
Improve efficiency and quality. Quickly extract valuable information from massive data, generate high-quality reports, suggestions, strategies, etc., save manpower and time costs, and improve the efficiency and quality of financial services.
Enhance innovation and competitiveness. Use massive data to mine potential market opportunities, risks and trends, provide financial institutions with new ideas and strategies, and enhance their innovation capabilities and competitiveness.
Reduce risk and cost . Use data analysis and simulation to predict market changes and risk factors, generate reasonable risk control and response plans, and reduce financial business risks and costs.
Enrich user experience and satisfaction. According to user behavior and feedback, adjust and optimize the generated content in real time, provide financial services that are more in line with user needs and preferences, and enrich user experience and satisfaction.
Enhance innovation and competitiveness. According to different needs and scenarios, generate diversified and personalized content to meet the diversified needs of customers and enhance the innovation and competitiveness of financial products and services.
Despite the great potential of AI tools, there are also some challenges.
AI tools cannot take into account all factors, such as unexpected events, changes in market conditions, and human intervention. Additionally, there needs to be greater transparency about how these tools make decisions. When using these AI tools, one must also consider that the recommendations they provide may be biased or biased.
The application of generative AI technology in the financial field faces some challenges, which can be summarized as follows:
1. Data security and privacy protection. Generative AI technology requires a large amount of data as input and output, which involves the security of financial data and the protection of customer privacy. How to prevent data leakage, tampering, abuse, etc. is an urgent problem to be solved.
2. Technical reliability and explainability. Generative AI technology relies on complex algorithms and models, and the generated content may have problems such as errors, deviations, and inconsistencies, which affect its reliability and credibility. At the same time, its generation process often lacks transparency and explainability, making it difficult for users to understand its principles and basis, which affects its acceptability and monitorability.
3. Laws and regulations and ethics. The application of generative AI technology in the financial field involves some legal and ethical issues, such as copyright attribution, responsibility attribution, information authenticity, fairness and justice, etc. How to formulate reasonable norms and standards to protect the interests and rights of all parties is an issue that requires in-depth discussion.
In order to better promote and apply generative AI technology, both manufacturers and users are seeking better solutions. For example, OpenAI has launched a new privacy feature for its ChatGPT, which allows users to turn off their chat history, thereby making the conversation more private. With chat history disabled, users' data will not be used to train and improve OpenAI's artificial intelligence (AI) models.
In the future, as more manufacturers launch corresponding solutions such as data security, technical reliability, laws and regulations, coupled with the supervision and supervision of various social organizations, generative AI will become a powerful tool to help organizations digitally transform and upgrade.
Postscript: Introduce and effectively utilize generative AI technology
With so many benefits of generative AI, how should financial companies introduce this technology? This also requires developing a suitable solution based on the specific needs and goals of the enterprise.
Generally speaking, the following aspects need to be considered when introducing generative AI technology:
First, data preparation. Data is the foundation of generative AI technology. Enterprises need to collect and organize enough, high-quality, and representative data for training and testing of generative AI models. The source of data can be the business data of the enterprise itself, or data obtained from public or third-party channels. The format and type of data should also be selected and converted according to different generation tasks, such as text, image, audio or video, etc.
Second, model selection. The model is the core of generative AI technology. Enterprises need to choose the appropriate model architecture and parameters according to their own generation tasks and data characteristics. The selection of the model can refer to the existing research results and open source codes, or you can develop or customize the model yourself. The selection of the model should consider the performance, efficiency, stability, interpretability and other factors of the model.
Third, model training. Model training is a key step in generative AI technology. Enterprises need to use existing data to train and optimize the model so that it can learn the laws and characteristics of the data and generate new content that meets the requirements according to the input conditions. Model training requires a lot of computing resources and time. Enterprises can use cloud computing platforms or professional AI service providers for model training.
Fourth, model deployment. Model deployment is the application stage of generative AI technology. Enterprises need to deploy the trained model to the production environment, connect and integrate with other systems or platforms, and provide generative AI services for users or customers. Model deployment needs to consider factors such as model compatibility, scalability, and security. Enterprises can use technologies such as containerization or microservices to implement model deployment.
Finally, model evaluation. Model evaluation is a continuous improvement process of generative AI technology. Enterprises need to regularly evaluate and monitor the generation effect of the model, collect feedback and suggestions from users or customers, analyze the advantages and disadvantages of the model, and update or update the model according to the actual situation. Adjustment. Model evaluation requires the use of reasonable evaluation indicators and methods, such as manual evaluation, automatic evaluation, and comparative experiments.
After understanding the technical characteristics, advantages and disadvantages of generative AI, what we need to explore is how to effectively apply generative AI. The following points apply to all industries, including finance.
Clarify goals and needs. Different application scenarios have different goals and requirements, and it is necessary to select the appropriate generative AI model and parameters to achieve the best results.
Choose high-quality data. Data is the foundation of generative AI technology, and high-quality, high-relevance, and high-diversity data need to be selected to improve the quality and credibility of generated content.
Evaluate and optimize results. Generative AI technology is not perfect, and may generate wrong or unreasonable content. The generated results need to be evaluated and optimized to improve the accuracy and applicability of the generated content.
Comply with ethical and legal norms. Generative AI technology may involve sensitive issues such as copyright, privacy, security, etc., and ethical and legal norms need to be followed to prevent abuse or misuse of generative AI technology.