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This is a summary of an episode of Pioneers, an educational podcast on AI led by our founder. Join 2,000+ business leaders and AI enthusiasts and be the first to know when new episodes go live. Subscribe to our newsletter here.
TL;DR:
- Generative AI is transforming the financial services industry, offering new ways to optimize workflows and unlock revenue streams.
- AI adoption in fintech varies, with highly regulated industries like capital markets being slower to implement AI-driven solutions.
- Success in fintech AI requires startups to navigate long enterprise sales cycles and secure buy-in from key decision-makers.
- A human-centered approach to AI in finance ensures that technological advancements support rather than replace human expertise in decision-making.
- To stay competitive, financial institutions must invest in AI-driven innovations while maintaining a focus on security, particularly in areas like deepfake detection.
Before we dive into the key takeaways from this episode, be sure to catch the full episode here:
Meet Peter - VC at Illuminate
Peter Hung is a prominent investor focused on B2B fintech and enterprise software, with a deep commitment to fostering innovation in the financial services industry.
As an investor at Illuminate Financial, Peter works closely with founders to support their companies' growth, particularly in the areas of generative AI and financial technology. He is known for his hands-on approach, leveraging his network of strategic LPs, including major financial institutions like JP Morgan, Citi, and Barclays, to create valuable connections for startups.
With a keen eye for emerging technologies, Peter is particularly interested in how AI can optimize workflows and generate insights and new revenue streams in financial services.
Peter's approach centers on fostering long-term partnerships with founders, guiding them through the complexities of selling into large enterprises while helping them navigate the ever-evolving fintech landscape.
Generative AI in Finance
Generative AI is becoming a crucial tool for businesses across various sectors, not just as a product but also as a means to streamline internal processes.
Peter highlights platforms like Cursor AI, which have revolutionized how developers work by integrating AI into coding workflows, making tasks significantly more efficient.
While some sectors, particularly regulated ones like capital financial markets, might adopt AI more slowly, Peter predicts that in the next decade, businesses that fail to leverage generative artificial intelligence will struggle to remain competitive.
The ability to harness this technology is becoming essential for both product innovation and operational efficiency across industries.
“Generative AI becomes yet another toolkit, enabling new features and possibilities.” — Ankur Patel
Peter also emphasizes how the current generative artificial intelligence landscape resembles a modern-day "gold rush," with many startups emerging across pre-seed, seed, and Series A stages.
How to Choose Vendors for Generative AI in Finance
He notes that while many companies are building similar solutions, the key to selecting the winners lies in founders with a visionary approach.
Successful founders, according to Peter, are those who not only solve immediate problems but also anticipate future developments in their industries, creating long-term, innovative solutions.
AI Adoption in Fintech
AI adoption in fintech is rapidly transforming the finance industry, with generative AI playing a central role.
Peter discusses how fintech companies, whether natively AI-driven or not, are increasingly integrating AI into their processes to remain competitive. While not every fintech company will sell AI-based products, Peter asserts that most will leverage generative AI tools in areas like development, sales, marketing, and operations.
Initially, there were doubts about how AI tools like ChatGPT would impact work, but Peter highlights how teams now rely on generative AI in finance to enhance productivity.
However, the pace of AI adoption varies across sectors.
Highly regulated industries, such as capital markets, may be slower to incorporate AI due to the critical nature of their workflows and stringent compliance requirements.
He also stresses that traditional machine learning models still play an important role in fintech, offering benefits like better guardrails and confidence scores. In sum, AI is reshaping fintech, driving operational efficiency, and enabling companies to innovate faster.
Challenges in Enterprise AI
Challenges in enterprise AI, particularly in fintech, revolve around:
- long sales cycles,
- slow adoption, and
- the complexities of navigating large organizations.
Peter Hung emphasizes how selling enterprise AI solutions, especially to banks and financial institutions, involves a lengthy process that can take 18 to 24 months.
Although generative AI in finance is creating excitement, sales cycles remain lengthy, and securing contracts requires navigating several stages, including proof-of-concept (POC) trials and gaining sponsorship from key decision-makers.
Peter points out that many startups get stuck in the POC phase, unable to progress to full production contracts due to difficulties in accessing the right decision-makers.
He stresses the importance of establishing connections with individuals who control the budget and can make final decisions, as dealing with lower-level managers often leads to delays and repeated cycles of approval.
The “Generative AI Domino Effect” in the Financial Sector
Another challenge is that enterprises often hesitate to adopt AI solutions from early-stage startups, preferring to wait until other major institutions have taken the plunge.
This creates a domino effect: once one bank adopts an AI solution, others follow.
Additionally, existing relationships with major vendors like Microsoft or Google can stifle competition, as large institutions may prefer integrated solutions over standalone AI products. These factors make breaking into enterprise AI a complex and resource-intensive process for startups.
Deepfake Detection Technology
Deepfake detection technology is becoming a crucial focus, particularly in financial services, as deepfake attacks pose significant security risks.
These attacks can bypass voice authentication systems, allowing fraudsters to mimic executives or clients, leading to substantial financial losses.
For instance, a Hong Kong bank lost $25 million to a deepfake attack. Beyond financial damage, such breaches can severely harm a bank's reputation, undermining trust with clients.
“Deepfakes are a scary reality, and protecting against them is critical.” — Peter Hung
This technology is essential for protecting communication channels and preventing fraud in the era of generative AI.
Financial Services and AI
In financial services, AI is being adopted across two main areas: cost optimization and new revenue generation.
Peter Hung explains that most current AI applications in the industry focus on automating workflows and reducing manual labor, making operations more efficient.
- For example, in wealth management, companies like wealth.com are using AI to streamline estate and financial planning, a traditionally time-consuming and manual process.
- On the revenue generation side, Peter mentions a company called Thea Insights, which uses AI to improve the classification of public equities. Their technology enables a more granular approach to industry classifications, allowing for the creation of customized indexes, such as those focused on gaming or AI, unlocking new revenue opportunities.
As mentioned, AI is also playing a growing role in fraud detection, particularly in protecting communication channels from threats like deepfakes.
Financial institutions are recognizing that staying ahead of AI trends is critical not only for operational efficiency but also for maintaining client trust and safeguarding against new forms of fraud.
VC Role in Startup Success
The role of venture capitalists (VCs) in startup success is crucial, particularly when selling to enterprises.
Peter emphasizes how VCs can offer significant value beyond capital by facilitating introductions to key decision-makers within large organizations.
This "hand-in-hand combat" is essential for startups, as navigating enterprise sales often involves long cycles and complex approval processes.
“Sales cycles remain long despite the catalyst of generative AI” — Peter Hung
VCs with strong networks can directly connect startups to senior executives, bypassing lower-level managers who may slow down progress.
Additionally, VCs help startups prepare for enterprise sales by ensuring they are compliant, have the necessary legal documentation, and are ready to execute efficiently.
Advice for Startups in Fintech
Peter advises fintech startups to carefully consider their target market, recommending many begin by focusing on SMBs (small to medium-sized businesses) before expanding into enterprise.
Starting with SMBs allows startups to gain traction faster, receive clearer signals on product-market fit, and iterate based on customer satisfaction and feedback.
Enterprise sales, while lucrative, come with long sales cycles and complex processes, often requiring substantial resources and time.
Peter also emphasizes the importance of being prepared when selling to large enterprises, ensuring startups have the necessary compliance, legal documentation, and support in place to meet enterprise standards.
Building relationships with investors or advisors who can facilitate introductions to decision-makers is crucial for navigating enterprise sales successfully.
