Generative AI Ignites a Trillion-Dollar Market: Exploring the Unknown, Innovating the Future
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Generative AI has unveiled a new era of artificial intelligence. When 'robots' are no longer satisfied with analyzing existing knowledge but gain the ability to create 'unprecedented' new content, what disruptive changes will it bring to industries? Let's glimpse the future through two case studies.
This is a foreign bank with 20,000 employees, whose annual salary expenditure reaches as high as $2.1 billion. After the preliminary application of generative AI, personnel costs were directly reduced by 30%.
Another example comes from the wealth management sector.
A bank's wealth management products faced poor customer response and conversion rates. Bairong Yunchuang further introduced generative AI technology on top of decision-making AI to create a closed-loop ecosystem for customer management. After some time, compared to the bank's original model, the marketing response rate increased by 11 times, and AUM also saw significant growth.
01. Behind Financial Institutions' Adoption of Generative AI
These are real-life examples of the powerful capabilities of generative AI, which is redefining the financial industry. Major commercial banks are actively deploying it. Qian Bin, Vice President of Bank of Communications, stated that large financial institutions, including Bank of Communications, are actively promoting the commercial value of generative AI. Not long ago, Agricultural Bank of China launched its financial AI large-scale model product, ChatABC, and Industrial and Commercial Bank of China collaborated with multiple institutions to release a general-purpose model for the financial industry.
The pursuit of generative AI by major banks is not a fleeting trend but an effort to open a new frontier. Bairong Yunchuang believes that generative AI will have a broad and profound impact on the financial sector, particularly in asset management, where it will unlock an unprecedented incremental market.
Taking wealth management as an example, Boston Consulting Group predicts that by the end of 2022, China's wealth management market was worth approximately 217 trillion yuan, and it is expected to reach 344 trillion yuan by 2026, indicating enormous potential. However, there has long been a disconnect between supply and demand in wealth management.
From the demand side, wealth management involves extensive customer communication, risk assessment, asset allocation, and performance feedback, consuming time and effort for both advisors and clients, while limiting business expansion and user experience. From the supply side, advisors rely heavily on subjective judgment and experience in matching assets with risk preferences, despite the need for quantitative analysis.
Generative AI offers a breakthrough. It can simulate human thinking, create new content in text, voice, images, and video through massive knowledge training, and do so at low marginal cost. When trained on professional knowledge bases, generative AI enables machines to interact with clients more affordably, quickly, and professionally, directly impacting financial product transactions and conversions, bridging the last mile from digital infrastructure investment to tangible results.
Boston Consulting Group notes, 'For example, bank wealth managers can interact naturally with generative AI assistants to inquire about client interactions, holdings, and recent transactions. AI can then infer client preferences, recommend products, and provide comparisons. This reduces the need for managers to master complex digital tools, as AI handles the information processing, allowing managers to focus on evaluation and minor adjustments.'
For clients, wealth managers previously provided three key pieces of information: duration, amount, and returns. Generative AI now equips ordinary clients with highly professional, on-demand 'private advisors.' Through continuous interaction, clients gain a comprehensive understanding of products and market trends, reducing confusion and building trust in products and institutions.
For institutions, human wealth managers previously faced a cap of around 800 clients, leaving millions 'untouched.' Generative AI automates tasks like client insights, service matching, communication, asset allocation, and portfolio management, enabling scalable, personalized service for even millions of long-tail clients.
02. Leveraging the Incremental Market: Technology and Knowledge in Tandem
Thus, the gap between supply and demand will narrow, and generative AI will unlock a massive incremental market in wealth management.
However, unlocking this market requires more than just technology or products. It demands deep industry expertise, combining technology and knowledge. The earlier bank wealth management case exemplifies this need.
Bairong YunChuang has developed a wealth management solution that constructs a closed-loop ecosystem for customer operations at a bank. Within this ecosystem, both the technical capabilities of generative AI and insights into the financial industry have played crucial roles.
On the technical front, Bairong YunChuang utilizes generative AI to conduct multi-wave, multi-channel outreach tailored to different customer segments. Simultaneously, the generated content assists human customer service representatives in better engaging with clients.
Regarding industry insights, Bairong YunChuang conducted extensive preliminary research and identified that the bank faced challenges with business, system, and organizational silos, making it difficult to quickly respond to changing customer needs. To address this, Bairong YunChuang proposed solutions focusing on system enhancement and organizational optimization.
First, it helped the bank establish a closed-loop system encompassing digital insights, intelligent strategies, AI-driven operations, and agile iteration to guide the upgrade of its wealth management framework. Second, as an external change agent, it strengthened interdepartmental collaboration within the bank, effectively breaking down internal silos and fostering agile cross-departmental integration.
Ultimately, through the combined application of technology and expertise, the bank's wealth management business achieved the results mentioned earlier.
In a related context, Boston Consulting Group simulated scenarios of interaction between bank relationship managers and generative AI in one of its reports.