Fintech today feels like a race of interfaces, where product teams spend months building new features only to see them replicated in days. Yes, features may close temporary gaps, but lasting results depend on deeper mechanics, namely, AI working inside the processes that matter. And the statistics say companies already know this: nearly 80% of fintechs have deployed AI somewhere in their stack — impressive at first glance, but less so on closer look.
The point is that most firms use AI for surface tasks like basic service automation that saves time but don’t change outcomes, while the real game-changer is AI wired into the backbone — a fraud model that cuts false positives or a support system that halves resolution time.
That’s exactly why competitors can copy your interface or certain features, but not years of risk signals, transaction patterns, and feedback loops that make AI smarter inside your business.
The Core Flows Where AI Creates Impact
Saying that the real edge comes from AI in the backbone may sound vague, so the natural question is: how does this look in practice? It shows up in the flows that decide outcomes: onboarding, payments, and support.
Onboarding comes first. Without AI, the process is manual, slow, and full of friction — a recipe for drop-offs. By contrast, AI-powered models flag suspicious profiles in real time and let good customers through in seconds, cutting churn and increasing conversion. So, the difference between waiting days for approval and completing the process in under a minute speaks for itself in measurable gains for growth and retention.
Switch to the payment process, and the picture looks similar. False positives (when legitimate clients are blocked) act like a hidden tax on every merchant, sometimes costing more than fraud itself. Here, AI-driven authorization models, trained on proprietary transaction histories, learn to distinguish risky activity (say, a stolen card attempt) and merely unusual patterns, like a legitimate customer shopping from a new location. The result is higher approval rates, fewer chargebacks, and a P&L advantage that no extra “payment button” can deliver.
Support is the next flow, and often the one customers rely on most. Today, AI-powered triage can resolve routine questions on its own and route complex ones, with full context, to human agents. But technology alone doesn’t guarantee adoption — a recent case study showed that customers at first resisted, skipping the bot and asking for a live agent. Yet, once proactive AI proved it could resolve issues faster, handling times fell by nearly 40% and conversion rates improved. So, this shows a broader truth: initial distrust is natural, as novelties need time to earn confidence. And when trust builds, the feedback loop kicks in: every interaction strengthens the system and deepens customer loyalty.
Of course, AI creates advantages that compound, but the thing is that compounding works only if the foundation is solid. That’s where the next challenge lies: building AI that goes beyond efficiency — the one that’s trusted, accountable, and hence durable.
Accountability Is the Price of Durability
AI can only be a lasting edge if it’s trusted in the eyes of regulators and auditors, because without accountability, the same technology that delivers efficiency can quickly turn from advantage to liability.
But what puts that trust at risk? Bias in scoring, opaque models, even chatbots that misfire — each erodes the confidence fintech depends on. That’s why cutting costs with AI while ignoring governance is self-defeating: every biased approval, privacy breach, or compliance failure affects the reputation built over the years.
Regulators are already responding to these risks.
For instance, the EU AI Act, already in force with phased deadlines through 2026 and 2027, will impose strict obligations on high-risk financial AI systems. Meanwhile, Singapore’s MAS has its FEAT principles, with toolkits to test models for fairness, ethics, accountability, and transparency. These examples make it obvious that AI that can’t be explained, monitored, and audited has no place in finance.
As it becomes clear, the real mistake is to treat governance as a “tax paid at the end,” when in fact it has to be part of product design. Build AI this way, and it will be durable, able to withstand both regulatory and market scrutiny.
Wire AI Into the Core
We’ve found that efficiency in flows proves AI’s value, and governance provides the foundation. Now, the question is how to embed AI into core systems — here’s the playbook:
- Focus on the flows that matter. Shiny functions may impress at launch, but they don’t move the bottom line, so start with onboarding, payments, and support. In that case, if you aim AI at the core flows, you cut fraud, speed approvals, and win loyalty — results no feature tweak can match.
- Beyond efficiency, measure what compounds. Forget metrics like “time saved per query.” Track the numbers that stack over time: higher approval rates, fewer false declines, faster resolutions, and lower churn. Cosmetic metrics fade as quickly as the next feature race, while compounding ones create an advantage that only deepens with scale.
- Treat governance as design. Embed explainability, audit trails, and human overrides into the product itself. Keep in mind that governance is what makes the system trustworthy to both customers and auditors.
- Build AI into the backbone. Don’t treat AI as just a widget sitting above the product — it’s a dead-end. Instead, embed it into the decision points that drive outcomes: risk scoring, payment routing, and query triage. Interfaces can be cloned overnight, but models rooted in core systems and learning from proprietary data create an edge that competitors can’t simply copy.
Taken together, these steps build an advantage no feature race can match. Ignore them, and you’ll end up chasing the next interface tweak — faster, perhaps, but still going nowhere.
