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Yang Yang

2023 is known as the "first year of big models", which has set off a climax of the hot "Thousand Model Wars" and become the brightest new star of AIGC (generative artificial intelligence). These machine learning models with huge parameter quantities and complex structures are built by multi-layer deep neural networks with hundreds of billions of parameters. Through training with large amounts of data, they learn to recognize patterns and rules, can perform complex tasks, and help various industries improve work efficiency. At present, many companies have adopted big model tools in their daily work, which have become rare artificial intelligence assistants.

In addition to a few companies that specialize in the development of general large models, most of them are vertically applied in various industries. The financial industry is one of the fastest-growing fields of artificial intelligence. The recently published book "AIGC Reshaping Finance" written by Lin Jianming, a well-known artificial intelligence decision-making expert and practitioner in the field of financial technology, is praised by the industry as the first monograph in China to explore how AIGC is applied in the financial industry. The book covers the evolution of AIGC technology and typical application scenarios, AIGC methodology for improving internal and external efficiency of finance, AIGC supervision and safe use strategies in the financial industry, training large financial models from scratch and tips on the use of engineering training points, future human-machine collaboration, and the transformation path of the financial industry from digital finance to smart finance.

The author states at the outset: "AIGC has opened the AI ​​Age of Discovery and is arriving at a speed beyond expectations. AIGC is accelerating the formation of new quality productivity and will reshape the financial industry in the future." In terms of content and structure, from the technical principles of AIGC to various application cases, the author's selection of materials focuses on the perspective of corporate operations, highlighting the orientation of win-win results both internally and externally, and giving equal weight to social and economic benefits. It presents a panoramic view of the in-depth impact and application prospects of AIGC in the financial field, and provides financial industry practitioners with an important reference and action guide for mastering the practice of financial vertical big models.

According to experts' predictions, in just five to ten years, financial practitioners will face a completely new financial industry. Financial technology based on big data and artificial intelligence will profoundly change financial process management, products and services.

Technology empowers finance starting from Internet finance. Decentralized finance such as blockchain technology and encrypted digital currency has also been popular for a while. However, the technology that can really be quickly implemented and improve financial business capabilities and efficiency is still data-based artificial intelligence. AIGC technology is advancing at a rapid pace and has quickly found a wealth of application scenarios in the fields of banking, securities, insurance, and investment. Artificial intelligence has become knowledge and skills that financial people must understand.

The author of the book believes that technology is the biggest driving variable for the digital transformation of the financial industry. In this process, AIGC plays a key role and becomes a "catalyst" for internal production efficiency in the financial industry. It can improve the level of automated operations, data analysis efficiency, automatic generation of financial reports, risk management efficiency, and human-machine collaboration, bringing significant cost reduction and efficiency improvement to financial institutions. For example, in the field of intelligent operations, AIGC can process large-scale, highly complex data and accurately grasp customer intentions and needs, thereby improving the interaction model between financial institutions and customers. As financial services take on the characteristics of lightweight, scenario-based, and personalized, AIGC will also drive the financial industry's technological architecture to shift towards a highly secure, scalable, high-performance, and easy-to-maintain path. ‌

The author of this book has a solid IT technology background and nearly 20 years of experience in the development and management of relevant financial products. He can explain technical principles in a popular way and closely integrate them with practice, building a knowledge platform for mutual understanding between engineers and financial professionals. For example, the book systematically interprets the essence of AIGC and machine learning, and goes deep into its working mechanism, algorithm design and potential limitations. These chapters fill the knowledge blind spots of financial professionals; and in terms of AIGC practical application, through a series of real cases, technical readers can see how AIGC plays a role in various aspects of finance.

Finance is a strictly regulated industry, and security and compliance are prerequisites for all technological applications. This book discusses related issues at great length. The author points out: "In scenarios such as identity recognition, data collection, regulatory data reporting, risk monitoring and early warning, AIGC has played an important role in addressing the challenges of technology governance." The author suggests that regulators use the advantages of AIGC technology to process large-scale regulatory data, thereby accelerating risk identification and decision-making, resisting complex and changing market environments, and maintaining the stability and healthy development of the financial market.

The application of artificial intelligence in the financial industry also involves multiple ethical fields, such as science and technology ethics, financial ethics, etc. The book explores in depth the ethical challenges and regulatory issues in the application, and provides financial institutions with strategic recommendations for facing this new field. The author believes: "We must dialectically view the relationship between finance and productivity, widely learn from and absorb international governance experience, overcome security and privacy risks, adjust production relations, promote regulatory updates, solve problems such as talent shortages, and use trusted AIGC to respond to science and technology governance challenges and promote the sustainable and healthy development of new quality productivity in the financial field." At present, my country is at a critical juncture of AI technological revolution and industrial transformation. The financial industry should actively play an innovative leading role, break through the "ceiling" of key technologies, and take root in the "experimental field" of industrial scenarios.