Dominic Jainy stands at the forefront of the modern financial evolution, bringing a sophisticated understanding of how artificial intelligence and blockchain are fundamentally retooling the banking sector. As an IT professional with deep roots in machine learning, he has spent years observing the shift from legacy systems to the agile, API-driven architectures that now define the competitive landscape. With the first decade of digital banking focused primarily on proof of concept and customer acquisition, Jainy is now guiding the conversation toward a second decade defined by deep integration and strategic alliances. His perspective is particularly valuable as we witness the rise of embedded finance, a sector poised to reshape how every consumer and business interacts with their money.
The following discussion explores the strategic pivot neobanks are making as they move away from expensive direct marketing toward a partnership-led distribution model. We examine the critical role of modern infrastructure in enabling rapid deployment, the shifting economics of banking-as-a-service, and how the resulting data “flywheel” creates a sustainable competitive advantage over traditional institutions. Throughout the conversation, Jainy highlights the necessity of robust governance and the strategic choices between mass-market enterprise plays and specialized vertical integrations.
Neobanks are shifting from direct customer acquisition to distributing products through third-party platforms like gig economy or e-commerce sites. How does this shift alter a firm’s long-term unit economics, and what specific steps should a neobank take to ensure their infrastructure can handle these massive external user bases?
This shift represents a fundamental transformation in how we view the cost of doing business, moving from a model of linear customer acquisition costs to one where the marginal cost of adding a user is virtually zero. When a neobank embeds its “banking rails” into a platform that already serves millions of people, they bypass the grueling and expensive marketing cycles that defined the last ten years. To truly capitalize on the projected 31.53% compound annual growth rate in embedded finance through 2034, firms must transition from being just a destination to becoming the invisible engine behind every transaction. To prepare for this, a bank must first implement an API-first architecture that allows for instant scalability without human intervention at every step. They should then focus on automated compliance engines and banking-as-a-service components that can handle the sheer volume of a partner like an e-commerce giant with millions of shoppers. Finally, it is crucial to establish a robust API management platform that offers real-time monitoring to ensure that the infrastructure doesn’t buckle under the weight of external traffic surges.
Traditional banks often face months of compliance reviews and technical hurdles due to legacy systems, while modern neobanks leverage API-first designs to launch partnerships in weeks. What internal governance structures are necessary to maintain this speed without compromising regulatory standards, and how do you navigate the resulting compliance complexity?
The agility of a neobank isn’t just a technical achievement; it is a byproduct of a governance culture that treats compliance as code rather than a manual checklist. In my experience, the most successful firms are those that have utilized a portion of the $3.6 billion in UK fintech investment to build automated “compliance-by-design” frameworks. Instead of the traditional “stop-and-go” review process, these banks use real-time monitoring tools that satisfy regulators by providing transparent, instant data on every transaction. We see this working effectively in the UK, where the FCA’s approach to open banking provides a clear roadmap, yet the complexity remains high because you are managing the risk profiles of various third-party partners. Navigating this requires a dedicated middle-layer governance team that focuses specifically on partner risk, ensuring that while the technical integration takes weeks, the regulatory safety net is woven into every API call. This allows a firm to remain as nimble as a startup while maintaining the ironclad security and trust required of a licensed financial institution.
With the embedded finance market projected to see significant annual growth, revenue models are shifting toward transaction volumes and API access fees. How should companies balance revenue sharing with partners against their own margins, and what metrics best indicate a partnership’s health beyond simple transaction counts?
When looking at a market that reached $148.38 billion in 2025 and is climbing toward nearly $200 billion by next year, the negotiation of revenue shares becomes a high-stakes balancing act. Neobanks must recognize that while they provide the heavy lifting of the financial infrastructure, the partner platform owns the customer relationship and the front-end experience. A healthy partnership model typically balances a baseline API access fee to cover overhead with a tiered transaction-based revenue share that aligns the bank’s profit with the partner’s success. Beyond just looking at transaction counts, we look at the “stickiness” of the integration—how deeply is the financial product woven into the partner’s user journey, and what is the churn rate of those end users? We also track the “data yield per transaction,” measuring how much actionable insight we are gaining about user behavior, which is often more valuable than the immediate fee generated.
Powering financial services for external platforms generates vast amounts of transaction and income data from workers and consumers. How can neobanks leverage this information to refine their credit underwriting or fraud detection models, and what are the primary challenges in integrating data from diverse industry partners?
The true power of embedded finance lies in the “data flywheel” it creates, where every payment made to a gig worker or every purchase at a digital checkout serves as a data point to sharpen our predictive models. By powering payroll for thousands of freelancers, a neobank gains a granular, high-definition view of income volatility and spending habits that a traditional branch-based bank simply cannot match. This wealth of information allows us to build credit underwriting models that are far more inclusive and accurate, as they are based on real-time earnings rather than stale, once-a-month credit snapshots. However, the challenge is that every partner—whether they are in healthcare, travel, or retail—formats and structures their data differently. To overcome this, neobanks must invest heavily in sophisticated machine learning layers that can ingest and normalize disparate data sets, turning a chaotic flood of information into a structured asset that improves fraud detection across the entire network.
Some firms focus on massive enterprise clients while others target specific verticals like healthcare or travel. What are the trade-offs between pursuing a high-volume infrastructure play versus a deep, niche-specific partnership strategy, and how does this choice influence product roadmaps over a five-year horizon?
Choosing between an enterprise-wide infrastructure play and a niche-specific vertical strategy is essentially a choice between scale and margin. A high-volume play, such as providing the banking services for a global platform, offers a massive influx of users and data, but it often requires a more generalized product roadmap and yields lower margins per user. On the other hand, focusing on a vertical like healthcare allows a neobank to build highly specialized features—like tax-advantaged savings accounts or specific insurance integrations—which can command higher fees and create a more defensible market position. Over a five-year horizon, an infrastructure-focused firm will prioritize the scalability and reliability of its “rails,” while a vertical-focused firm will invest in deep product customization and sector-specific compliance tools. We see this diversity in the market today, with some players aiming to be the invisible backbone of the $1.76 trillion fintech market, while others strive to be the indispensable financial partner for a single industry.
What is your forecast for embedded finance?
I believe we are rapidly approaching a reality where banking becomes entirely contextual, disappearing into the background of our digital lives so completely that the term “banking” might start to feel obsolete. With the sector projected to grow at a staggering 31.53% CAGR, we will see a shift where financial services are no longer something you “go to” but something that is “just there” the moment you need it, whether you are buying a flight, starting a freelance job, or paying for medical care. This expansion will likely lead to a massive consolidation of data power, where the neobanks that have built the most robust partnership networks will hold a machine-learning advantage that is impossible for traditional laggards to replicate. In the next decade, the winners won’t be the banks with the most branches or even the best standalone apps; they will be the ones that have successfully woven themselves into the fabric of every other industry. We are looking at a future where financial inclusion is driven by data-rich, invisible infrastructure, making high-quality banking services accessible to everyone, everywhere, at the exact point of need.