By James Lichau, Partner, Assurance, Financial Services Co-leader, BPM
As someone who works closely with fintech companies across their lifecycle—from seed-stage payment processors to growth-stage lending platforms preparing for exits—I have a front-row seat to how AI is fundamentally reshaping this industry. Not in some distant future, but right now, with implications that will fully materialize in 2026.
The conversations I’m having with fintech founders and CFOs have shifted dramatically in the past year. We’ve moved beyond “should we explore AI?” to “how do we implement it responsibly while maintaining unit economics?” This isn’t just another technology cycle. AI is becoming embedded infrastructure, particularly in three critical areas: payment orchestration, fraud detection, and the economics of embedded finance.
AI-powered payment orchestration: Beyond simple routing
Payment orchestration has traditionally been about routing transactions to the right processor at the right time. Smart, but relatively straightforward. AI is changing this from a reactive routing engine to a predictive optimization system that learns and adapts in real-time.
Here’s what this looks like in practice: imagine a payment platform processing thousands of transactions daily across multiple geographies. Traditional orchestration might route based on preset rules—processor A for credit cards, processor B for ACH, with basic failover logic. AI-powered orchestration analyzes patterns across success rates, processing costs, settlement times, and even subtle signals like time-of-day trends or seasonal variations. It predicts which routing path will optimize for both conversion rates and cost efficiency before the transaction even happens.
The implications for 2026 are significant. A fintech company might see authorization rate improvements of 2-3 percentage points, which might sound modest until you calculate what that means for a platform processing billions in annual volume.
But here’s where it gets more interesting: AI-powered wallets. We’re moving toward digital wallets that don’t just store payment credentials but actively manage how and when to use them. These wallets will predict your optimal payment method based on merchant, purchase type, and available rewards, while factoring in real-time fraud risk assessments. The wallet becomes your financial decision-making agent, not just a repository of cards.
Fraud detection and credit intelligence: The arms race accelerates
If there’s one area where AI adoption in fintech is non-negotiable, it’s fraud detection. The sophistication of fraud attempts has increased exponentially, and rule-based systems simply can’t keep pace. One key area to be aware of is “adversarial learning. “Fraudsters are using AI too, which means your defenses must evolve just as rapidly.
Traditional fraud detection relied on historical patterns: if a transaction matched known fraud signatures, flag it. AI-powered systems analyze hundreds of variables simultaneously—device fingerprinting, behavioral biometrics, transaction patterns, network analysis—to identify fraud that’s never been seen before. This is particularly crucial for synthetic identity fraud, where criminals create entirely new identities rather than stealing existing ones.
What makes 2026 particularly important is the maturation of collaborative intelligence. Fintech companies are beginning to share anonymized fraud signals across platforms, creating industry-wide AI models that benefit everyone. A fraudulent pattern detected on a B2B payment platform can inform risk models at a consumer lending platform, provided the data sharing frameworks respect privacy and competitive boundaries.
For credit intelligence, AI is transforming underwriting from a backward-looking credit score to a forward-looking risk assessment. Alternative data sources—cash flow patterns, subscription payment history, utility payments—feed AI models that can assess creditworthiness for segments historically underserved by traditional credit scoring. One lending platform has expanded their serviceable substantially while maintaining lower default rates by incorporating AI-driven cash flow analysis into their underwriting.
The challenge, and it’s a significant one, is explainability. Regulators rightly demand that lending decisions be fair and transparent. An AI model that says “this applicant is high risk” without explaining why creates both compliance problems and reputational risks. The fintech companies succeeding here are investing heavily in explainable AI—models that can articulate, in human terms, which factors drove specific decisions.
Embedded finance: Cost efficiency meets ubiquity
Embedded finance—the integration of financial services into non-financial platforms—has been growing for years, but AI is fundamentally changing its economics. The cost of offering payments, lending, or banking services within your existing product has historically been high enough to limit adoption to larger platforms. AI is driving those costs down dramatically.
Consider the traditional path for a software company adding embedded lending: integrate with a banking partner, build compliance frameworks, underwrite risk, manage collections. Each step requires significant infrastructure and specialized talent. AI is automating much of this stack. Underwriting becomes algorithmic, compliance monitoring becomes automated, and collections strategies adapt dynamically to borrower behavior.
This is playing out with vertical SaaS companies serving industries like healthcare, real estate, and professional services. They’re embedding payment processing, working capital loans, and even deposit accounts into their core software, using AI to manage the entire financial services operation with minimal human intervention. This isn’t just convenience—it’s a fundamental shift in software economics. Financial services become a natural extension of the value proposition rather than a separate business line requiring dedicated infrastructure.
The fraud detection capabilities I mentioned earlier become even more powerful in embedded finance contexts because the platform already has deep behavioral data about its users. An accounting software platform knows its customers’ cash flow patterns, invoice timing, and seasonal variations. When that platform adds embedded lending, it possesses far richer data for underwriting than a standalone lender ever could. AI turns this contextual data into competitive advantage.
Navigating the ethical and regulatory landscape
None of this happens in a vacuum, and it shouldn’t. The FinTech Bloom article on AI ethics highlighted critical considerations that apply directly here. Bias in AI models remains a persistent concern, particularly in credit decisioning and fraud detection. An AI model trained on historical data will perpetuate historical inequities unless explicitly designed to counter them.
For fintech companies preparing for institutional investment or strategic transactions, AI governance has become a standard due diligence item. Investors want to understand not just whether you’re using AI, but whether you can demonstrate fairness, explain decisions, and adapt as regulations evolve. The companies positioning themselves well for 2026 are building AI ethics frameworks now, not waiting for regulations to force the issue.
Transparency will increasingly become a competitive differentiator. Fintech platforms that can explain their AI-driven decisions in plain language—whether it’s why a payment was flagged for additional verification or why a loan application received specific terms—will build stronger customer relationships than those hiding behind algorithmic black boxes.
Preparing for 2026: Practical considerations
For fintech leaders considering where to invest in AI, I’d offer this perspective based on what I’m seeing work across our client base:
Start with your highest-friction, highest-volume processes. Payment orchestration and fraud detection typically offer the fastest ROI because the data exists, the use cases are clear, and the impact is measurable. Don’t build everything in-house—leverage specialized AI platforms for payments and fraud, then differentiate on the layer above.
Build explainability into your models from day one, not as an afterthought. This matters for regulatory compliance, customer trust, and your own ability to improve the models over time. An AI system you can’t explain is one you can’t effectively manage.
Think about AI implementation as infrastructure, not features. The most successful fintech companies I work with treat AI capabilities as foundational technology that improves everything they do, rather than discrete features to market. This changes how you budget, hire, and build.
Finally, recognize that your competitors and your banking partners are making similar investments. AI adoption in fintech isn’t about gaining competitive advantage anymore—it’s about maintaining competitiveness. The question isn’t whether to implement AI-powered payment orchestration or fraud detection, but how quickly and how well.
The reality of 2026
A year from now, the fintech landscape will look notably different from today. AI-powered payment optimization will be table stakes. Fraud detection will be an arms race between increasingly sophisticated attacks and defenses. Embedded finance will have expanded into verticals we haven’t yet imagined, enabled by AI that makes the economics work.
The fintech companies that thrive will be those that implement AI thoughtfully—balancing innovation with responsibility, automation with explainability, and efficiency with trust. The technology is ready. The question is whether the industry’s leadership and governance frameworks can keep pace.
