The future of AI in finance – and the crucial role human intelligence will play

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Here’s a reality you might find surprising: AI in finance is not a new phenomenon. In 1982, Renaissance Technologies ushered in the AI era with pioneering “expert systems” for financial data analysis and investment decision-making. Since, the finance sector has widely employed predictive AI for the examination of past market figures, patterns and signals.

The advent of generative AI, or GenAI, is set to be one of the biggest shakeups since such technologies were introduced. This is because so much of what the sector does is language-based, creating greater potential to leverage large language models (LLMs). Recently, McKinsey estimated that GenAI could automate up to 70% of business activities in finance, translating into vast productivity gains.

Nonetheless, adoption doesn’t come without risks; these include the potential for biased outcomes stemming from flawed or unrepresentative data, difficulties in ensuring data privacy and security, and the considerable financial investment required for implementation and maintenance. But, perhaps most importantly, it’ll be a lack of in-house expertise that will hinder effective adoption. Undoubtedly, external support will play a critical role, fundamentally changing the organisational makeup of a number of businesses and institutions.

Thus, perhaps the industry’s most relevant question is: How is AI changing finance? Here, we’ll discuss the future of AI in finance, examining the early gains, the state of play, and projections for the future, including any roadblocks we see on the way. AI will change the landscape of financial services completely, and it won’t only be the early adopters who reap the benefits – it’ll be those with their wits about them and the right expertise at their side.

How is AI used in finance?

Upon initial exploration, GenAI’s potential to reshape financial services was abundantly clear. The technology’s impact on efficiency highlighted numerous possibilities across functions, including sales, customer and staff interactions, fraud and risk management, and product development. To give but one example, the AI advocacy platform All About AI estimates that AI-powered tools can process transactions 90% quicker, and their fraud detection features offer similar gains in speed and accuracy.

Consequently, it is unsurprising that top firms are investigating, investing in, and implementing GenAI at a considerable pace. In light of AI’s influence on most financial service functions, there has been a significant increase in the creation of specialised solutions. These are being developed both internally and by technology providers such as OpenAI, Google, Microsoft and Amazon, among others.

As initial trials continue to produce encouraging results, the industry’s dedication is anticipated to grow. Projections from the World Economic Forum suggest that by 2025, AI could lead to savings of $200 to $340 billion and generate $450 billion in revenue, with investments in GenAI reaching $1.68 billion.

Expanding the horizons

The vast potential of AI to generate efficiency gains is self-evident, and this is reflected in investments and applications. This inclination is understandable, as cost-reduction programmes tend to yield short-term, quantifiable results compared to initiatives aimed at growth or risk mitigation. 

Nonetheless, further research by the World Economic Forum suggests that 70% of financial services executives believe that AI will directly contribute to revenue growth in the coming years. This is linked to capabilities far beyond efficiency gains; as leaders continue to identify and prioritise specific AI applications, several common themes consistently emerge.

One prominent area is the delivery of personalised customer experiences. Here, AI-powered virtual assistants can provide comprehensive and tailored support around the clock for customers’ routine inquiries, such as product recommendations or application processes. For more complex issues, these AI tools can augment the capabilities of human agents, enabling them to deliver quicker and more pertinent responses.

Another key theme is product innovation and revenue growth. GenAI, in particular, is enabling financial services firms to address specific market niches more effectively; one such example is advisory services for the mass-affluent segment, which was previously impractical. It is also fostering innovation and the creation of entirely new revenue streams, for instance, by combining synthetic customer data with more efficient A/B testing methodologies for new deposit and lending products. 

Finally, AI is significantly enhancing risk management, compliance and security functions. AI systems can scrutinise transactions and other relevant events with thoroughness and speed far surpassing human capabilities. It is also being deployed to continuously monitor cybersecurity threats and identify suspicious activities in real time. However, these tools are not only in the hands of companies; they are increasingly deployed by malign forces.

Addressing challenges and risks of AI in finance

Despite considerable enthusiasm for AI in finance, its implementation isn’t without reservations. As previously mentioned, these threats can be intentional, with malicious actors leveraging AI for cyberattacks or other illicit activities. However, risks can also arise unintentionally from well-meaning users, such as the introduction of unintended biases into credit underwriting algorithms. Given the powerful capabilities of most AI tools, their potential for harm isn’t to be ignored.

This apprehension contributes to a more cautious adoption of AI in areas directly impacting the customer experience, where concerns about cyber threats, market manipulation, data bias and privacy pose substantial challenges. Nevertheless, as companies gain more experience with customer-facing AI applications, their confidence in managing these associated risks is also growing.

A significant risk associated with AI in finance is the proliferation of misinformation, which can lead to market manipulation and fraudulent transactions – a growing concern across all industries. A particularly serious threat stems from GenAI’s ability to create and disseminate synthetic content, including sophisticated deepfakes. This risk has escalated with the increasing accessibility of advanced GenAI, evidenced by a substantial surge in deepfake-related tool trading on dark web forums.

In response to these inherent risks, efforts to establish effective AI policy and regulation are progressing, remaining a high priority for business leaders globally. Countries and regions are actively working towards a standardised set of guidelines, with various groups initiating the definition and publication of foundational frameworks. Notable examples include the AI methodologies published by the Monetary Authority of Singapore (MAS), the comprehensive European Union’s AI Act, and the recent AI Executive Order from US President Biden.

However, concerns persist regarding the inconsistent approaches of regulatory bodies worldwide, especially when comparing the EU’s extensive AI Act with the US executive order. Equally, regulations themselves, while vital for stability and consumer protection, pose inherent challenges for financial services. The need to adhere to stringent reporting requirements and demonstrate compliance adds to administrative overhead, potentially diverting resources from core business functions and innovation. Meanwhile, regulatory complexity and rapid change add uncertainty and impede cross-border activity.

Firms will find themselves in fierce competition for a limited pool of experienced generative AI specialists and must proactively cultivate their own talent pipelines.

Plugging the AI skills gap

For AI to truly revolutionise financial services, the focus must extend beyond algorithms and data to the crucial role of people. The industry faces a significant challenge: bridging the widening AI skills gap. Experts are needed not only to build and deploy these complex systems but also to collaborate effectively with them and, in some instances, oversee their operation. As GenAI becomes increasingly commonplace, akin to essential software, a fundamental shift requires all employees to embrace the technology and learn to leverage it for optimal performance.

Understanding employee sentiment towards AI and its impact on workplace dynamics will be paramount for executives as they chart their future course. Open and honest communication about a firm’s AI strategy, directly addressing worker anxieties around job security and evolving roles, will be a vital step towards fostering effective human-AI collaboration at scale.

Subsequently, financial services firms will find themselves in fierce competition for a limited pool of experienced generative AI specialists and must proactively cultivate their own talent pipelines. This includes recognising and training for entirely new specialisations, such as prompt engineering. Moreover, continuous training for all employees across all levels will be essential as new AI-powered tools are introduced and upgraded. Even senior executives will need to acquire a deep understanding of AI’s current and potential contributions to shape and execute successful business strategies.

In this landscape, the role of external experts – specialised AI consultants, trainers, and niche technology providers – will be crucial in supplementing internal capabilities, providing cutting-edge knowledge, and accelerating the development of the necessary skill sets across the organisation. Talent platforms like Outvise offer a cost-effective and efficient avenue for financial services firms to connect with such specialised expertise on demand. Leveraging such platforms can significantly expedite the upskilling process and provide access to knowledge that might be scarce internally, proving invaluable in navigating the complexities of AI in finance and bridging the critical skills gap.

Towards an adaptable, scalable AI future

The growing integration of AI in finance, coupled with the increasing value derived from AI strategies, suggests a significant transformation for financial services in the coming decade. Further digitalisation of platforms, smarter automation, and more interactive, decision-oriented workflows are expected to reshape how customers engage with banking, investment, borrowing and insurance services.

Customers can anticipate enhanced experiences through AI’s ability to synthesise vast datasets, including financial history and personal milestones, leading to more efficient interactions and relevant advice for long-term financial planning. Improved customer support is also on the horizon, with smart AI agent chatbots providing audio and video responses and the capability to action custom requests autonomously, delivering faster and more accurate solutions.  

To realise these advancements and keep pace with rapid innovation, financial services leaders will require adaptable AI strategies and continuous monitoring of the evolving technology landscape. Key areas to watch over the next two to three years include: 

• Small language models (SLMs) that provide instant and accurate answers to customer inquiries about specific financial products, improving customer support efficiency.
• Retrieval-augmented generation (RAG) to enhance chatbot accuracy by allowing them to access and cite internal policy documents when answering complex compliance-related questions.
• AI Agents for automating routine account management tasks, such as processing fund transfers based on customer instructions, without human intervention.
• Quantum computing will significantly accelerate the detection of complex fraud patterns in transaction data, leading to more effective security measures.

Access the AI expertise you need

AI in finance will soon become part of the industry’s operational fabric. As these technologies become central to business strategy, executives must actively evaluate IT ecosystems to seize emerging opportunities, ensuring that AI investments are thoughtfully integrated into broader initiatives.

While these powerful AI technologies offer immense potential, the critical role of human expertise, particularly from external specialists, cannot be overstated. Navigating the complexities of AI in finance, from strategic planning to ethical considerations and talent development, requires seasoned professionals with specialised knowledge. Their objective insights and experience are invaluable in ensuring successful integration and maximising the return on AI investments.

For financial institutions seeking to harness the full power of AI while efficiently addressing their expertise needs, Outvise offers a compelling solution. We provide direct access to a global network of highly skilled AI experts, enabling firms to tap into the precise knowledge required to navigate the evolving landscape and achieve their AI ambitions. If you haven’t signed up already, do so now, and connect with game-changing experts who are local to you.