AI & Generative AI: latest trends in applied AI for Financial Services

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I recently had the privilege of attending a panel discussion during the Hong Kong FinTech Week 2023 on the topic of "AI & Generative AI: latest trends in applied AI for Financial Services."

The panel brought together industry leaders from major financial institutions including Michael Demissie , Chak Wong , Daniel Li 李濱 , James Lee and Yusuf Demiral 鄧裕斯 , Group Head of Data, Analytics and CRM, Wealth and Personal Banking 汇丰 to discuss their experiences and perspectives on AI and its applications in the banking sector.

Key takeaways

One of the key takeaways from the discussion was the transformational impact AI is having on financial services. One consensus was that AI is accelerating the need for companies to transform digitally. Survey data showed that within the next decade, firms that failed to adapt would be unviable. AI was highlighted as the primary catalyst driving this change.

The panellists agreed that while the core functions of banking - like serving customers, managing risk, and trading - remain the same, AI is changing how these activities are performed.

Defining solvable business problems and determining AI feasibility pose enormous challenges requiring cross-discipline cooperation. Also, bridging the gap between AI hype and realities will better help evaluate risks and return on investment opportunities.

Realizing AI's full potential takes overcoming both technological and organizational change barriers through patience, experimentation, and continuous learning.

Additional key Takeaways

1. AI is seen as the primary driver of digital transformation, which surveys show is necessary for long-term business viability over the next decade.
2. Companies adept at responsibly leveraging AI are projected to gain competitive advantages.
3. Current AI uses include enhancing customer experience via personalized tools and improving fraud detection by analysing digital behaviours.
4. Privacy, transparency and aligning AI capabilities to well-articulated issues rather than speculation are important considerations.
5. Over the next year, AI systems are expected to become more seamlessly embedded across operations rather than standalone point solutions.

Few examples, and real-life use cases

HSBC is using AI to provide over 2.2 million mobile banking customers with personalized financial insights and budgets to positively impact their spending behaviours.

JPMorgan is leveraging AI across knowledge management to better assist employees. Bank of China emphasized the importance of AI maintaining privacy and ensuring transparency about its recommendations.

What Challenges?

From an operational perspective, several challenges around AI implementation were highlighted. Chak Wong from JPMorgan drew an analogy that putting a computer scientist and a business person together to work on a project, like translating texts between languages they don't understand, often leads to bizarre results.

Defining problems correctly and determining feasibility is hugely difficult. Most of the work involves understanding problems rather than developing complex algorithms. Strong cross-discipline cooperation is needed to operationalize AI solutions.

Panellists urged focusing on well-defined business issues rather than technologies alone. AI should serve as a means to solve targeted problems, not an end itself. Starting with clear objectives and success metrics was advised, in addition to knowing core competencies and partnering strategically. Liquidity forecasting aided by AI to optimize investing represented a discussed application.

What perspectives for tomorrow

Looking ahead one year, the panel offered several perspectives and predictions

• AI capabilities will be seamlessly embedded in all operations rather than separate point solutions.
• The hype around AI will align more with realities so risks and opportunities are better understood.
• Companies that properly leverage AI to will "win."
• Continuously educating entire workforces on the evolving AI-driven culture.

While exciting progress has been made, fully realizing AI's potential will still take time. If we look at the progress done in the self-driving cars space for 30 years (already), and we guess that we are still 10 years away.

The core technologies have existed for decades but real-world applications require infrastructure and cross-discipline understanding to develop.

An advice to all of use is experiment, learn, and focus on fundamentals rather than just using tools superficially.

In summary, the panel painted a picture of AI's immense transformative power for financial services when properly addressed as an enterprise-wide, cross-functional effort focused on well-defined business problems rather than just technologies. Continuous learning will be critical as capabilities advance rapidly while execution challenges remain substantial. Overall, AI represents a significant opportunity for forward-thinking companies committed to responsible innovation.

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