Recently, Li Zhengyuan, senior expert of Zhanrong Digital Solutions, and Lai Shoufang, a financial risk control model expert of Zhanrong Digital, brought a wonderful live broadcast to the industry on the WeChat video account "Zhanrong Digital". They systematically analyzed the current situation of the application of big language models in the field of financial risk control, and discussed the development potential and resistance of big language models in this field in the future.
Financial institutions, especially banking institutions, still have relatively high requirements for explanatory, effective and accurate credit risk control, and are more inclined to have specific and explainable computational logic models. At present, the explanatory and stability of large models are still difficult to evaluate. Although some progress has been made in the combination of big language model and financial risk control model, there has been no obvious improvement.
Lai Shoufang believes that the future financial risk control model will continue to develop in the direction of intelligence and digitalization, especially with the rise of new technologies such as deep learning, blockchain, and large models, financial data will be more transparent and reliable, which will provide a more accurate data basis for the use of large model technology for financial risk control. At the same time, large language models should also properly solve the problems and challenges of data privacy and security issues, model interpretability and reliability, and training and optimization for specific financial scenarios.
The following are the main contents of this live broadcast sharing:
Circle of competence for large language models
The basis of a large language model is a language model, so its limits are also limited by the limits of language models. Therefore, understanding and grasping what a language model can do usually means knowing the scope of the ability of a large language model.
In general, we divide the things that language models can do into three categories:
The first category is information form transformation.
In short, it is the conversion of information from one form to another, whether it is speech recognition or machine translation, which falls into this category. In speech recognition, the input information is speech sound waves, and the output information is text, they correspond one-to-one, so it is the formal transformation of information. The same is true of machine translation, which is the encoding of one language to the encoding of another. It should be noted that any form of information conversion usually loses some information.
One application that usually goes unnoticed in this category of things is in the medical field, such as gene sequencing. In addition, writing simple programs with the help of computers also falls into this category.
The second category produces content upon request
Similar to the main work that ChatGPT does today, that is, to produce text according to instructions, such as answering questions, replying to emails, and writing simple paragraphs, all fall into this category.
In this type of work, the amount of information input is significantly less than the amount of information output. From the point of view of information theory, this creates a lot of uncertainty and requires additional information. The source of supplementary information is actually the information contained in the language model. Therefore, if a language model contains a lot of relevant information about a topic, it can produce high-quality text; Otherwise, the answer it gives or what it writes may be fantastic.
The third category is the condensation of information summaries
It is to reduce more information into less information. For example, writing an abstract for a long article, conducting data analysis as required, and analyzing the financial reports of listed companies are all part of this work.
In this type of work, there is more input information and less output information, so as long as the algorithm is done well, there will be no problem of insufficient information. By changing information from more to less, you are faced with a choice between which information to keep and which to delete. Similarly, streamlining more information will also yield different results, depending on how the algorithm is designed, what kind of information the language model it relies on has counted before, and so on.
The past and future of the financial risk control model
Financial risk control models are very important tools in the financial industry to evaluate lending applications, credit scoring and risk management. From the perspective of history, bank risk control has experienced the evolution from the traditional model in the past to intelligent risk control and then to the current digital risk control.
The traditional approach of risk control is more based on manual approval of expert experience, relying on the understanding and grasp of the materials by the loan examiner and giving the final opinion. Assuming that two loans are approved at the same time, the loan examiner will make a decision based on experience, based on the ranking, and draw his own conclusion, choosing loan A or loan B.
With the advancement of science and technology, especially the development and application of big data, more and more weak variables are integrated into the risk control system, changing the single strong variable risk assessment. Especially with the emergence of new fraud methods, banks and other financial institutions have gradually developed and formed an intelligent risk control technology system on the basis of the application of automation, big data, cloud computing, artificial intelligence, blockchain and other financial technology fields.
Whether it is a traditional system or an intelligent risk control system, its core is data. Combined with the latest intelligent risk control technology, comprehensive digital management of data can be achieved to build a comprehensive digital risk control system. For digital risk control, its core looks at two capabilities: one is data accumulation, and the other is technical capabilities.
Risk decision automation is an advanced manifestation of digital risk control, and decision-making has been fully automated in the field of credit card approval and small amount consumer finance.
AI helps the exploration and prospect of financial risk control
At present, AI technology is increasingly used in the field of financial risk control, and financial risk control is also experiencing a leap from computational logic to wide-area thinking logic, among which the application of large language models is becoming more and more extensive, which is specifically manifested in the following three aspects:
The first is intelligent customer service and user experience. With the advancement of AI technology, large language models can realize the function of intelligent customer service through natural language processing and semantic understanding. It can understand users' questions and give accurate answers, provide personalized financial advice and support, and improve the user experience.
This is followed by anti-fraud and security monitoring. Big language models can identify potential fraud and cyberattacks through the analysis of internal data and user behavior data. It can help financial institutions monitor user transaction and behavior data in real time in the private domain, detect abnormal situations in time and take corresponding anti-fraud and security measures.
Finally, risk assessment and forecasting. Big language models can improve the accuracy of risk assessment and prediction by learning and analyzing massive amounts of financial data. It can identify potential risk factors and correlations, helping financial institutions better assess loan default risk, credit card fraud risk, and more. At the same time, large-language models can also provide financial institutions with more accurate profit forecasts and market volatility forecasts.
In the future, the financial risk control model will continue to develop in the direction of intelligence and digitalization. With the rise of new technologies such as deep learning, blockchain, and large models, financial data will be more transparent and reliable, which will provide a more accurate data basis for the use of technologies such as large language models for financial risk control.