Embedded finance is undergoing a fundamental shift. What began as Banking-as-a-Service (BaaS) – exposing core banking features like payments, accounts, and cards via APIs — has evolved into something more intelligent and experience-driven. Financial services
no longer operate quietly in the background. They now adapt in real-time, shaping user journeys with contextual, personalized intelligence powered by AI.
In our AI-led consulting work with global enterprises, this evolution is clear. What was once “bolted-on” banking is now a strategic lever for differentiation. According to the latest
BCG Global Fintech Report, fintech revenues surged by 21% in 2024, with 69% of publicly listed fintech now profitable. This showcases strong evidence that AI-enabled, embedded finance is
fast becoming the new standard.
Beyond BaaS: Intelligence at the Core
In practice, this evolution means moving beyond generic modular components toward deeply embedded financial capabilities that anticipate and respond to user context in real-time.
In healthcare, we’ve seen AI used to assess patient eligibility and create personalized payment plans at the point of care, reducing friction and improving affordability. Retailers now deploy instant, AI-driven credit decisions at checkout, increasing both
conversion and customer lifetime value. In SaaS, embedded payments and subscription analytics powered by AI are boosting per-user revenue and enhancing trial-to-paid conversion rates.
These are not isolated use cases; they reflect a broader trend. Enterprises are no longer embedding tools; they are embedding decision-making directly into customer journeys. This marks a critical shift from banking-as-a-service to intelligence-as-a-service.
Data Is the New Competitive Edge
As financial institutions consider charging third parties for access to customer data, the economics of embedded finance are being redefined. Data, especially high-quality, permissioned data, is no longer a commodity. It’s a premium asset.
This makes the role of AI even more critical. With fewer data signals available, platforms must extract greater value from each one. Enterprises that succeed will be those that invest in AI models tailored to deliver actionable insights from each data point,
doing so ethically, transparently, and efficiently.
Overcoming the Roadblocks to Embedded Intelligence
Despite the promise, most enterprises face three key challenges in scaling intelligent embedded finance:
- Legacy infrastructure: Many still operate on monolithic systems not designed for real-time, API-first interactions
- Unclear monetization: Organizations struggle to translate embedded finance into measurable business value
- Complex regulatory landscape: AI introduces new scrutiny around fairness, explainability, and data privacy, often across multiple jurisdictions
According to us, the following strategy can help enterprises overcome the challenges:
- Start with user journeys: Identify the real moments where intelligent finance can remove friction or unlock value
- Integrate AI from day one: Intelligence is not a feature to be retrofitted. It should shape design decisions from the beginning
- Design for data sovereignty: Embrace architectures like federated AI that keep sensitive data local, minimize exposure, and comply with evolving privacy laws
- Build trust-based ecosystems: Transparent data-sharing frameworks are key to scaling embedded finance across partners
- Adopt iterative, cross-functional delivery: Teams spanning product, engineering, risk, compliance, and legal should work side-by-side, guided by KPIs that measure real business impact, not just technical milestones
Banks: Compete, Collaborate, or Enable? The Answer Is ‘All of the Above’
For traditional banks, embedded finance presents a strategic crossroads. The most successful are pursuing a blended strategy:
- Competing selectively where they have unique customer relationships or product strengths
- Collaborating with fintech and platforms to tap into new user bases and accelerate innovation
- And most importantly, enabling, i.e., offering secure, intelligent APIs that serve as the infrastructure layer for embedded financial services across industries
Banks that embrace their role as intelligent enablers, building platforms with AI-powered decisioning, real-time monitoring, and usage-based monetization, are carving out durable relevance in the new ecosystem.
Responsible AI Is Non-Negotiable
The promise of intelligent finance depends on trust, and trust depends on responsible AI. We advise clients to:
- Ensure explainability: Models that determine credit decisions or flag fraud must be understandable to both customers and regulators
- Actively mitigate bias: Continually test, monitor, and refine algorithms to ensure fair financial access for all segments
- Implement robust data governance: Privacy, security, and responsible usage must be embedded into every system
- Keep humans in the loop: Especially in edge cases, human oversight adds critical judgment and accountability
- Embed security from day one: Protecting sensitive financial data is a baseline requirement, not a future feature
The Window Is Narrowing
The future of embedded finance will not be defined by who integrates the fastest, but by who embeds the most intelligence responsibly, securely, and at scale.
As data grows more expensive and user expectations rise, banks, fintech, and enterprises must evolve from service providers to orchestrators of financial intelligence. That means investing now in cloud-native platforms, AI-first design, and API-led architectures
that can adapt, learn, and scale.
The opportunity is immense, but so is the urgency. Institutions that move decisively today will shape the financial infrastructure of tomorrow. The time to lead is now.
