McKinsey: The financial consulting gap in Hong Kong is widening, and AI applications for financial advisors are lagging behind the speed of industry reshaping

The Zhitong Finance App learned that on September 8, the latest survey released by management consulting firm McKinsey showed that the financial consulting gap in Hong Kong is widening. On the one hand, about half of customers already use artificial intelligence (AI) before making purchasing decisions; but on the other hand, half of financial advisors use AI less than once a week or even never, and the pace of financial advisors is clearly not keeping up with the speed of AI reshaping the industry.

According to the survey, about 80% of customers who buy major life, health and wealth management products still rely mainly on financial advisors, but the reasons behind it are practical rather than emotional: 43% want someone to understand their personal or family situation, 41% want help in making major decisions to avoid wrong decisions, 35% need immediate answers to complex issues, and 34% want someone to take responsibility after purchasing.

In terms of AI usage scenarios, customers increasingly view AI as their “second opinion”, most commonly used to identify potential financial and security needs (35%), understand and compare related products (34%), and make purchasing decisions (30%). As to why customers use AI, 58% mentioned more objective and consistent comparisons, 53% mentioned richer knowledge, 39% mentioned lower sales orientation, and 37% mentioned being more able to connect personal circumstances with insurance needs.

However, the use of AI on the financial advisor side is clearly lagging behind. Half of the consultants use AI less than once a week or never, yet most are still willing to let AI play a bigger role in their work. In current work situations where AI is not used, “lack of appropriate AI tools” is more commonly mentioned than “feeling uncomfortable using AI”. The only exception is the development process of finding potential customers and customer sources — consultants have a strong sense of protecting front-line customer relationships.

This discovery reshaped the industry's way of thinking about the AI challenge: the core is not the resistance of financial advisors, but the lack of the right tools. If financial institutions can provide more relevant tools, practical scenarios, and more clear evidence of benefits, the application process of Al can be accelerated.

Shi Weilin, managing partner of McKinsey Global and general manager in Hong Kong, said that AI has not eliminated the need for insurance consultancy advice; instead, it has raised the standards that must be met for good consulting. Customers often obtain information, comparisons, and questions provided by AI when meeting with consultants, but they still want the consultant's judgment and responsibility for major decisions. He believes that the real watershed may be between consultants who make good use of AI to improve their performance and those who don't use AI.

Hu Ziliang, global managing partner of McKinsey and head of financial services sales and distribution business in Asia, added that AI can only provide low-level collaboration, complex consulting or after-sales services, and still requires financial advisors. He also said that for decades, the core of competition among financial institutions has always been the professionalism and productivity of consulting teams. AI has the potential to improve both at the same time, but only if it is embedded in critical moments that are critical to customers. In his view, the opportunity is not simply to deploy more software, but rather to establish an AI-enabled consulting model — combining more adequate preparation with personalized service, and human professional judgment and responsibility.

To address these challenges, the report also puts forward four suggestions: first, financial institutions should invest in training, mentoring, and reshaping the work model of financial advisors on the same level as investment in technology; second, the effectiveness of AI depends on its underlying system, and institutions should lay a good data foundation before expanding; third, incorporate the expertise of top financial advisors into the system; and fourth, invest in human skills that AI cannot replicate.