The growth of AI for fintech has increased rapidly over the past several years, reaching more than $14 billion in 2024. The sector is expected to clock in at close to $18 billion in 2025, reflecting a compound annual growth rate (CAGR) of nearly 26%. Going forward, experts project an even steeper trajectory (31% CAGR) to more than $52 billion by 2029.
Up to 2024, much of this growth came from investments in things like risk management, fraud prevention, automation of repetitive tasks, and data analytics. In 2025, we’re witnessing the widespread adoption of robo-advisors, chatbots, and virtual assistants that measurably improve productivity, service quality, and cost reduction. Moreover, we can expect heightened interest in AI for compliance, credit score modeling, upsell and cross-sell, and cybersecurity advancements.
The financial services industry is notoriously commoditized, risk adverse, and highly regulated. On top of these baked-in realities, industry leaders are contending with a workforce that’s accustomed to work-from-home privileges. Jamie Dimond, Chairman of the Board and CEO at JPMorgan Chase, is leading the charge to compel employees to work full-time in the office, citing concerns such slower decision-making, stagnant innovation, and lack of managerial oversight.
In addition to these recently enacted HR measures, forward-thinking businesses are leveraging AI investments to help build competitive advantage based on better customer experiences and efficiencies. Because AI offers a clear path to this goal, we expect tighter iteration cycles for development, implementation, and testing, which will result in transformative operational advancements in the industry.
Improving Service Delivery
In a traditional financial services model, salespeople are incentivized to move a prospect down the funnel as quickly as possible. However, financial services customers often require multifaceted discussions and education on the details before signing off on transactions. When human agents are managing these inquiries, it’s prohibitively expensive to take people through these steps, particularly at scale.
Conversely, virtual assistants can service your customers throughout the buhying journey at a low fixed cost and no need for human involvement.
AI-enabled tools can source curated content, and — using retrieval automated generation (RAG) — adapt some of those materials within the context of the customer profile. Additionally, the technology facilitates linking the specifics of the customer’s needs to the appropriate products, services and configurations.
For customers who feel anxious about the knowledge and information gap, interacting with financial services advisors can be intimidating. For younger customers, such as Gen Z, the pressure to interact with a human and process information over the phone in real time can be stressful. In contrast, when you pull these experiences into AI-enabled virtual assistants, these customers can digest information at their own pace. Using the always-on technology, they can ask as many questions as they want — without feeling judged — to gain more context before deciding.
Because financial services inquiries are consequential, optimizing programmatic responses is crucial. Using agentic AI, you can improve on one-size-fits-all suggestions. This type of AI system understands the goal, vision, or problem a user is trying to address in context and makes tailored recommendations. Applying these tools, you can create customized experiences for specific market segments.
Designing Better Content Experiences
Because financial services organizations are subject to regulatory requirements, they’ve developed solid systems for tracking and observability — this means they have extensive knowledge of customers and their preferences. Historically, these large and diverse data sets have presented challenges for product managers and designers.
Although they have skilled teams producing great content for financial literacy, many organizations struggle with finding and providing the appropriate information for specific customers. In many cases, friction in the search process prevents your teams from providing these mission-critical resources that help you close business.
In some cases, it’s informational overload for your representatives; in other cases, customers don’t know what to ask for, or how to articulate questions.
Tools that use natural language processing, such as AI chatbots, understand and respond to customer inquiries in simple language, making it easier for users to access information without worrying about what they don’t know. Additionally, AI-enabled search and recommendation systems can improve internal search, helping agents quickly find what they’re looking for.
If you ask customers to complete a form, they’re unlikely to give up those details because they dread the phone calls and emails. Fortunately, AI can collect pieces of information from micro interactions without directly asking for it, empowering you to enrich customer profiles in a manner that feels helpful yet unintrusive.
Building Customer Trust
When making investments in AI-enabled tools, you’ll need to get ahead of the predictable concerns your customers will bring up when it comes to how your business is handling AI, and what measures you’ve implemented to protect them. Luckily, there’s plenty to talk about.
There’s already some confidence baked into the system related to regulatory protections (e.g., CCPA, GDPR, and HIPAA). Financial services organizations who do this well are elevating data security and privacy protocols to a core branding pillar and avoiding legalese. As your customers ease into the maturity curve and experience the AI advantage for themselves, you can build trust and affinity over time.
Customers will ask where their data is stored, and where it’s processed. Gaining greater control over data transparency has been trending in the B2B space for a while, and that ethos is already trickling down to B2C consumers. Anticipate financial services will be leading the charge, with innovators transforming this capability into a formalized framework to gain competitive advantage.
Moving Forward
At the beginning of the AI revolution, the CIO owned the AI story; now the CFO and others are involved because investments must be vetted from multiple perspectives. Currently, we’re in a proliferation stage where various tools are doing the same thing and creating conflict — subsequently, the orchestration layer will become more important.
When you’re sourcing AI-enabled tools from multiple providers, lean heavily on these vendors and partners to understand their existing regulatory frameworks, and how well they’re working with other vendors. Sometimes partnerships are dysfunctional — if you get caught up in this situation, put the onus back on the partners and vendors to solve it for you.
It helps to understand where you can push harder in your organization because you have solid support, and where you need to take a step back and apply a portfolio approach. In financial services, you need to know the point people in relevant departments, and who’s looking cross-functionally across the AI estate. Ownership of these AI investments remains nebulous, and you can expect the structure will be different for each organization — all in all, AI for fintech will be an exciting space to watch.