The most tech-savvy industry seems to be stalling on AI
The financial industry has been a leader when it comes to digitalisation - but when it comes to transferring this towards AI we see that the industry is actually lagging behind.
‘'AI Reality Bites' - Every day, new advancements in AI are announced - but what do they mean in practice?
Here's what we know: AI has tremendous potential to re-engineer fundamental banking aspects, from risk assessment to fraud detection to credit evaluation. AI can make banking fairer, faster, and more trustworthy — all values that should underpin the global financial system. So where is the catch? Somehow the financial industry is not adopting AI at the necessary speed. First, it is important to get a better picture of how FinTech and the traditional financial sector work.
How Fintech embraced digitalisation
To better understand the FinTech sector, it is helpful to model the industry along the lines of B2B and B2C. In the B2C domain, customers have come to anticipate and demand a seamless digital experience, expecting intuitive interfaces, rapid response times, and secure yet accessible transactional processes.
The proliferation of tech penetration in the B2B segment, on the other hand, serves as the infrastructural backbone supporting this B2C digital prowess. B2B FinTech solutions streamline operational workflows, enhance data analytics capabilities, and fortify security measures, thereby indirectly elevating the end-user experience in the B2C space. Consequently, the synergy between B2B and B2C aspects is instrumental in shaping a holistic, efficient, and customer-centric FinTech ecosystem.
Although the financial industry is usually known for conservatism, it has managed to adopt new technologies over the last 20 years and is today one of the most digitized industries. However, despite the burgeoning possibilities sprouting from the intersection of AI and FinTech, we can see a measured, perhaps cautious integration, especially within the core banking infrastructures. AI's prowess is evident in customer service, where chatbots deliver 24/7 real-time support. However, its deeper integration into the very nucleus of banking operations remains notably measured.
What is keeping the industry from unleashing AI
AI adoption is stuck because of the structural setup of established players. The prevailing architectural structures within traditional banking institutions present a significant operational challenge. Often, data within banks are segregated into silos, a practice adopted to enhance security by restricting access to specific data sets and preventing any widespread impact in case of a security breach.
Consequently, accessing these disparate silos, each intertwined with its legacy infrastructure poses a colossal hurdle on a broader scale. This segregated data architecture inhibits the seamless integration and flow of information across various departments and functions, limiting the ability to rapidly innovate and implement new solutions that respond to evolving market demands and customer expectations. For these reasons, we cannot expect the AI transformation to come from within the industry but instead must focus on startups that are not locked into these old ways.
"Tech penetration in the financial industry has peaked, ideally positioning it for robust AI development. However, legacy infrastructures hinder the journey toward an AI-empowered future." Anouk Moll, a seasoned FinTech entrepreneur and current entrepreneur-in-residence at Merantix, told me. Anouk's expertise helped me deepen my understanding of the FinTech landscape related to AI, which I'll lay out here.
The potentials that Generative AI bring
It's easy to see the potential of Generative AI in finance, a robust data-focused industry. FinTech has had AI applications, typically oriented towards prediction and classification, often predominantly leveraging time-series data. However, with the advent of generative AI, an exhilarating possibility unfolds to harness even the vast swaths of unstructured data, blending it seamlessly into decision-making and predictive modeling processes.
Indeed, given the advanced digitization and the resultant availability of extensive historical data, Foundation Models can be fine-tuned to decipher, understand, and generate insights from data that was previously deemed challenging to integrate into analytical models. Generative AI doesn't merely analyze; it creates, imagines, and proposes, enriching the analytical prowess of traditional models with an additional layer of innovative spontaneity and capability.
By employing generative AI, financial institutions can delve deeper into the intricate web of diverse data, unraveling insights and strategies that are both data-driven and innovative, paving the way for a future where finance is not only intelligent but also intuitively adaptive and forward-thinking.
The signs for a change
Despite this technological boom, AI, as it stands today, has only scratched the surface of its potential utility within the banking sphere, showing how deep the structural issues lie. But the tides are about to change. Recently, Open Banking has transformed the financial ecosystem in Europe by enhancing transparency, customer-centricity, and data value through third-party developer access. Stringent yet transformative regulatory frameworks such as Know Your Customer (KYC) and Anti Money Laundering (AML) have propelled organizations towards a tech-forward approach, making AI a critical element in ensuring compliance and maximizing the utility of existing technological infrastructures and rich data.
With its transformative capabilities, generative AI autonomously, for instance, creates formulas and queries in financial forecasting or Business Intelligence to streamline analyses and identify patterns, reducing the need for manual inputs and facilitating a more systematized approach towards understanding and leveraging financial data.
The new boom we experience with this technology is paving the way towards a future where predictive and intuitive decision-making coalesce to redefine operational norms within FinTech. Yet, its fullest realization may very well hinge on the entities that drive its implementation and evolution — a role where startups are surprisingly pivotal.
For Anouk, that problem is front of mind as she looks into different ideas: "One thing is the post-trading space, which is known for the high amount of institutional players due to high regulatory requirements. It is a fascinating space that has seen some innovation in the US with companies like Alpaca, but Europe has seen minimal movement within this space. The other area I'm looking into is FX wallets for high inflation markets and using gen AI to empower people to safeguard their savings through accessing stable currencies."
There are many more challenges in FinTech that need to be taken on, and an increasing number of startups are working on it. The interesting situation here is that AI is often the solution to AI applications' challenges. This recursive feature of AI makes it such a promising technology for FinTech. So let’s have a look at whats already been done:
Challenges and Solutions in AI and Fintech
In case you wonder what some main challenges are and who is already working on them. Here is a brief and non-exhaustive overview:
Challenge: Ensuring Robust Data Security
Financial institutions are gatekeepers of a plethora of sensitive data, and especially in regions like Europe, stringent regulations are in place regarding the sharing and utilization of customer data, acting as a substantial barrier to deploying AI solutions seamlessly.
Solution:AI comes full circle as a solution by being applied extensively in cybersecurity to mitigate these concerns. The need to develop in-house AI solutions for financial institutions creates a fertile market for developer tools, ensuring data is protected while being utilized optimally.
Examples:RESISTANT.AI: Software to protect AI systems from advanced fraud (backed by GV and Index Ventures)Comply Advantage: ComplyAdvantage provides AI-driven financial crime risk data and detection technology. (backed by Balderton.)
Challenge: Navigating Complex Regulation and Compliance
The European Union regulations surrounding KYC and KYB add complexity to customer onboarding. The recent AML measures in the EU necessitate meticulous scrutiny over capital movements, while fragmented markets introduce variability in compliance requirements across jurisdictions.
Solution: AI's capacity to synthesize vast data and information becomes pivotal in ensuring compliance with varied and stringent regulations. Its ability to implement and enforce AML guidelines while ensuring user-side compliance becomes instrumental. Moreover, AI enables financial bodies to adapt to different market regulations and requirements without a consequential resource or time allocation increase.
Examples:onfido: AI to acquire new customers and reduce costs while meeting global KYC and AML compliance (backed by Tech Nation and TPG)Signzy: AI-powered FinTech company offering a digital onboarding solution for banks, NBFCs, and more (backed by Gaja Capital and Tenity)
Challenge: Enhancing User Accessibility and Experience
Making financial services intuitive and secure, especially while integrating into other services without compromising user experience (UX) or security, presents a significant hurdle.
Solution:AI can enhance UX by enabling content personalization and adapting interfaces to match user knowledge and comfort levels. Moreover, its capability to understand user behavior and adjust product offerings accordingly allows for a more tailored and intuitive user experience. Furthermore, predictive risk and fraud models can be developed through AI, facilitating proactive intervention and resulting in a secure user experience, yet not intrusively so.
Examples:ExpressSteuer: AI-powered software automating the process of filing a German tax return (backed by Insight Partners and Project A)Cleo: Personalized financial advisor (backed by EQT and SOFINA)
Looking towards the future
The very nature of AI, with its data synthesis, predictive modeling, and adaptive learning, positions it as a formidable tool to address the complexities and challenges that pervade the financial sector, paving the way for a future where Fintech is not only more secure and compliant but also user-centric and accessible.
The symbiosis between financial technology and Artificial Intelligence presents the promise of an era of unparalleled possibilities and innovations.
From nuanced, personalized customer experiences to robust, unassailable cybersecurity, AI is not merely an adjunct but a powerhouse propelling FinTech into new dimensions of efficacy and customer-centricity. Navigating through the intricate labyrinth of regulatory frameworks, data security, and global financial ecosystems, AI emerges not as a mere technological tool but as a compass guiding FinTech towards more equitable, accessible, and transparent financial futures.
Thanks for reading, and please let me know what you think, what further opportunities in the market you see or what technological trends on the horizon you believe will make an impact. A special thanks goes out to Eduard Hübner, who was my great co-author for this piece.
- Rasmus
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