Imprint’s Unicorn Moment: Scaling Co-Branded Cards

Co-branded cards are having a quiet resurgence—right when most people assumed loyalty programs had peaked. Brands want direct relationships, banks want sticky balances, and consumers want rewards that actually match how they shop. The surprise isn’t that a co-branded card provider reached unicorn status. The surprise is how fast the infrastructure layer is scaling.

Imprint hitting unicorn status (a private valuation of $1B+) is a strong signal about where payments infrastructure is headed in 2026: brands are becoming fintech distributors, and the winners will be the platforms that can launch programs quickly while keeping fraud, underwriting, and servicing under control.

This is where the “AI in Payments & Fintech Infrastructure” story gets practical. When you’re onboarding new brand partners, underwriting new cardholders, routing transactions, and handling disputes across multiple programs, you don’t just need modern APIs—you need intelligent automation that reduces loss, improves approval rates, and protects customer experience.

Why co-branded card infrastructure is scaling again

Co-branded cards are scaling because they sit at the intersection of three things that are growing fast: embedded finance, loyalty economics, and data-driven personalization.

First, brands are under pressure to improve margins. Many retailers and marketplaces are dealing with rising acquisition costs and fickle demand. A successful co-branded card can shift behavior (more frequent purchases, higher average order value) while turning loyalty into a profit center.

Second, consumers are reward-fatigued. Generic points programs blur together. Co-branded rewards tied to real spend categories (travel, fashion, grocery, creator platforms) stand out—especially when redemption is easy.

Third, modern card program managers and co-branded card providers have reduced the “time-to-launch” burden. Historically, co-branded programs took forever because of complex bank partnerships, processor coordination, compliance, and servicing. Today’s infrastructure providers package that complexity.

Here’s the stance I’ll take: the next wave of co-branded cards won’t be won by whoever offers the flashiest rewards. It’ll be won by whoever runs the safest, most efficient risk and operations stack at scale.

What a unicorn co-branded provider really signals

A unicorn valuation isn’t a trophy; it’s a bet by investors that a company can scale distribution and unit economics. For a co-branded card provider, that typically implies three capabilities:

1) Partner velocity without operational chaos

Signing brand partners is the fun part. The hard part is executing repeatably:

• Program design that fits each brand’s customer base
• Compliance and disclosures that don’t slow everything down
• Customer servicing that doesn’t crater NPS when volumes spike
• Ongoing marketing experiments that don’t create risk blind spots

Unicorn status suggests the platform has found a system to launch and operate multiple programs without each becoming a bespoke engineering project.

2) Underwriting and fraud controls that scale with growth

As portfolios grow, fraudsters notice. They probe weaknesses in identity verification, application flows, and dispute handling. At the same time, the business needs approvals to stay healthy.

That tension—approve more, lose less—is exactly where AI in payments can create durable advantage.

3) An infrastructure narrative, not a single-program narrative

The co-branded market used to be dominated by a few massive programs. The new model looks more like a portfolio of mid-sized programs across verticals. That’s an infrastructure play.

And infrastructure businesses live or die on reliability: uptime, chargeback rates, fraud rates, authorization performance, and customer experience.

Where AI fits: scaling co-branded cards without scaling losses

AI isn’t a nice-to-have when you’re operating multi-partner card infrastructure. It’s how you avoid hiring your way into a cost problem.

AI fraud detection for co-branded card portfolios

Answer first: AI improves fraud detection by spotting abnormal patterns across applications, transactions, and account behavior faster than rules alone.

In co-branded portfolios, fraud patterns vary by partner. A travel brand will see different attack vectors than a fashion marketplace or a subscription service. A static rules engine tends to either:

• block too much (hurting approvals and customer experience), or
• miss novel fraud (increasing losses)

Practical AI techniques that matter here:

• Behavioral anomaly detection (spend velocity, merchant mix, device consistency)
• Graph-based signals (shared devices, emails, addresses across applications)
• Real-time risk scoring during authorization (not just at application)

A strong operating principle: fraud models should be portfolio-aware and partner-aware. You want shared learning across the network, with partner-specific tuning to avoid false positives.

AI-supported underwriting: higher approvals, controlled risk

Answer first: AI-supported underwriting helps approve more good customers by incorporating richer signals while maintaining conservative loss targets.

Co-branded programs often attract “thin file” customers—people who are creditworthy but don’t show up clearly in traditional bureau views. That’s especially true for younger shoppers and international newcomers.

AI can assist by:

• using alternative signals (cash-flow, payroll, consistent bill pay patterns)
• improving identity resolution (reducing synthetic identity approvals)
• detecting first-party fraud (intentional non-payment behavior patterns)

This doesn’t mean “let the model decide everything.” The better approach is human-in-the-loop for edge cases and model governance, with clear decline/approve explanations for compliance and customer trust.

Transaction optimization and routing intelligence

Answer first: AI can reduce declines and increase authorization rates by optimizing routing decisions and retry logic.

As co-branded programs scale, authorization performance becomes a growth lever. A few basis points of approval-rate improvement can translate into meaningful revenue, especially during peak periods.

Examples of AI-enabled transaction optimization:

• Smart retries (when and how to retry a soft decline)
• Dynamic routing (choosing pathways that historically approve better for certain MCCs or issuers)
• Latency-aware decisioning (balancing speed and risk checks)

This is one of the least glamorous parts of payments infrastructure—and one of the most profitable when done well.

AI in customer experience: disputes, servicing, and retention

Answer first: AI reduces servicing costs while improving cardholder experience by automating routine tasks and surfacing the right context to agents.

Co-branded programs live and die on the customer experience. If redemption is confusing or disputes are mishandled, customers blame the brand—not the infrastructure provider.

AI can help by:

• categorizing disputes and pre-filling claim data
• detecting likely “friendly fraud” patterns to reduce unnecessary refunds
• powering support tools that summarize account history instantly
• identifying churn risk (e.g., reward frustration, repeated declines)

Here’s a simple truth: support tickets are operational debt. AI helps you pay it down before it compounds.

The hidden risks in co-branded card growth (and how to manage them)

Unicorn growth stories gloss over operational risk. If you’re building or buying fintech infrastructure, these are the failure modes that actually matter.

Model risk and compliance aren’t optional

If AI touches underwriting, fraud, or disputes, you need:

• model governance (versioning, monitoring, drift detection)
• explainability appropriate to the decision type
• bias testing and adverse action logic where required
• audit trails for regulators and bank partners

If you can’t explain your decisions, you’re borrowing time.

Data fragmentation across partners can break intelligence

Co-branded programs run across multiple systems: bank partners, processors, KYC vendors, customer support platforms, loyalty engines.

AI only performs as well as the data pipeline:

• consistent event schemas
• reliable identity resolution
• clear definitions for fraud labels and dispute outcomes

A practical recommendation: build a unified “cardholder timeline” that merges application, KYC, auth, clearing, payments, disputes, and support events. That’s the substrate for analytics and AI.

Fraud migration is real

When you strengthen one control (say, application fraud), attackers shift to another layer (account takeover, refund abuse, dispute manipulation).

The fix isn’t “more rules.” It’s layered defense with feedback loops:

1. identity proofing + device intelligence
2. application risk scoring
3. real-time authorization risk scoring
4. post-transaction monitoring
5. dispute and chargeback analytics

A practical playbook: using AI to scale co-branded cards

If you’re a fintech, issuer, processor, or brand evaluating a co-branded program, here’s what works in practice.

1) Start with one measurable KPI per layer

Pick targets you can measure weekly, not quarterly:

• Application: approval rate, fraud rate, manual review rate
• Transactions: authorization rate, soft decline recovery
• Portfolio: loss rate, delinquency roll rates
• Operations: cost per ticket, time to resolution

AI projects fail when success is fuzzy.

2) Ship decision support before full automation

Use AI first to:

• prioritize reviews
• flag anomalies
• recommend actions to agents

Then automate the repeatable decisions with tight monitoring. This reduces regulatory and operational risk.

3) Treat every partner launch like a model stress test

Each new brand changes the customer mix and fraud surface area. Plan for:

• warm-up periods where models adapt
• partner-specific thresholds
• extra monitoring during the first 60–90 days

4) Build feedback loops from disputes and chargebacks

Disputes contain high-quality labels when handled well. Feed outcomes back into:

• fraud models
• merchant-level risk scoring
• customer experience interventions

If disputes are siloed as “customer support,” you’re missing one of the best learning channels in payments.

People also ask (and the real answers)

Are co-branded cards still worth it for brands in 2026?

Yes—if the program is designed around incremental behavior (frequency, retention, and higher-margin categories), not vanity metrics like sign-ups.

Where does AI provide the fastest ROI in card programs?

Fraud detection and servicing automation usually pay back first because they reduce direct costs and loss. Underwriting improvements can be bigger—but they require stronger governance.

What should you ask a co-branded card provider before signing?

Ask about authorization rates, fraud and chargeback performance, model governance, dispute handling SLAs, and how quickly they can iterate rewards without breaking compliance.

What Imprint’s unicorn status means for AI in payments infrastructure

Imprint reaching unicorn status is a reminder that payments infrastructure companies can scale quickly when distribution is embedded inside trusted brands. But that same distribution magnifies risk: fraud attacks intensify, support volumes spike, and underwriting mistakes become expensive.

If you’re building in this space—or selecting vendors—the practical lesson is straightforward: AI should be embedded in the operating system of the card program, not bolted on after growth arrives. Fraud models, authorization optimization, dispute intelligence, and customer experience automation are the difference between “fast growth” and “durable growth.”

The next twelve months will reward teams that treat AI as part of core payments infrastructure. If your co-branded strategy depends on scaling without scaling headcount and losses, what part of your stack is still running on rules and spreadsheets?