Ramp’s SmartPay Shot: AI Expense Controls at Scale

The U.S. government runs an internal expense card program called SmartPay, and it’s massive—about $700 billion in annual purchase volume. That number isn’t just big; it’s operationally punishing. Every basis point of waste, every policy exception, every manual reconciliation step multiplies across agencies, offices, and vendors.

That’s why the recent news that Ramp is being considered for a U.S. General Services Administration (GSA) charge card pilot is more than a “startup lands a pilot” story. It’s a signal that AI in payments and fintech infrastructure is moving from corporate back office optimization into the hardest environment of all: public-sector spend, where auditability beats aesthetics and reliability beats hype.

If you’re building, buying, or modernizing payment infrastructure—issuer processing, expense controls, fraud systems, or compliance automation—this is the kind of pilot worth paying attention to. Not because Ramp is special in a vacuum, but because government-scale payments force the real questions: security, policy enforcement, data quality, and operational resilience.

Why SmartPay scale changes everything

SmartPay’s scale forces automation, because humans can’t review everything. In enterprise expense programs, you can sometimes get away with sampling, manual reviews, and exceptions handled by a small team. In a program that touches countless cardholders and transactions, manual processes don’t “strain”—they collapse.

At SmartPay scale, the problem isn’t just fraud. It’s cost management, policy compliance, merchant risk, and post-transaction cleanup (matching, coding, receipts, approvals, disputes). Even if fraud is statistically rare, the surface area is enormous.

The hidden tax: reconciliation and coding

In many organizations, the biggest cost of an expense program isn’t interchange, card fees, or even fraud losses. It’s the ongoing labor required to:

• Categorize transactions correctly for reporting and budgeting
• Collect receipts and validate them against policy
• Route approvals and handle exceptions
• Prepare for audits and respond to findings

This matters in government because appropriations, program budgets, and audit requirements are not “nice-to-haves.” They’re the core operating constraints.

The pilot tells you what procurement now values

A government pilot implies a short list of priorities that tend to dominate selection:

1. Control strength (can you prevent bad spend, not just flag it?)
2. Audit-grade reporting (who approved what, when, based on which policy?)
3. Security and vendor risk posture (data handling, access controls, incident response)
4. Integration reality (can it fit into messy, legacy workflows?)

If a modern expense platform can meet those demands, it’s a strong indicator that the infrastructure bar for AI-powered fintech is rising—and buyers will expect the same capabilities in the private sector too.

What Ramp’s government push signals about AI in payments

The real story is that AI is becoming “middleware for spending decisions.” Not a chatbot. Not a dashboard. A decision layer that sits between a card swipe and an approval trail.

When an expense platform competes for government-scale programs, AI has to do the unglamorous work:

• Normalize merchant data
• Predict coding fields (GL, cost center, project)
• Detect anomalous spend patterns early
• Enforce policy automatically, with explainable rules

A useful way to think about it: AI doesn’t replace controls; it industrializes them.

AI expense management isn’t just automation—it’s control loops

Most teams treat expense management as a workflow: spend → submit → approve → reimburse/report.

At scale, the better model is a control loop:

1. Pre-spend controls: Who can buy, from where, with which limits
2. Real-time decisioning: Block/allow, step-up approval, dynamic limits
3. Post-spend learning: Update policies based on observed patterns
4. Audit and evidence: Store decision rationale and approvals

AI can strengthen steps 2 and 3, but only if the system is built for traceability. Government buyers tend to demand that the system can answer: Why was this allowed? Who changed the policy? What data did the system use?

Transaction routing and network decisions can become smarter

In the broader “AI in Payments & Fintech Infrastructure” series, a recurring theme is that routing intelligence is now a competitive advantage—across authorization, disputes, and fraud. For expense programs, routing isn’t just “which rail.” It can also mean:

• Routing transactions into the correct approval lane
• Routing anomalies to investigators with the right context
• Routing certain merchant categories into pre-approved budgets

The best infrastructure does this without creating a helpdesk nightmare. If every edge case becomes a ticket, you didn’t modernize—you just relocated the pain.

The security and compliance bar for public-sector card programs

Government card programs don’t tolerate black boxes. If AI is involved in controlling spend or detecting fraud, the system has to be defensible—to auditors, inspectors general, and procurement stakeholders.

That changes how fintechs need to design AI features.

What “explainable” actually means in an expense control system

In practice, explainability isn’t a philosophy. It’s a set of artifacts you can produce on demand:

• The policy rule that applied (e.g., MCC blocked, dollar limit, time-of-day limit)
• The inputs considered (merchant, amount, cardholder role, location, historical pattern)
• The action taken (approved, declined, step-up approval required)
• The human override trail (who overrode it and why)

A strong stance: if your model can’t produce evidence, it doesn’t belong in the decision path for public funds.

Fraud detection: fewer false positives beats louder alarms

In a corporate program, a flood of alerts is annoying. In a government program, it can be politically and operationally dangerous because it creates:

• Backlogs that hide real fraud
• Reduced trust in the system
• Pressure to loosen controls

AI-powered fraud detection must be tuned for precision, not theatrics. The goal is to stop clear abuse and surface high-risk anomalies—without blocking legitimate mission-critical purchases.

Data governance is part of the product

Public-sector buyers will evaluate vendor posture around:

• Role-based access controls and least-privilege design
• Data retention and deletion policies
• Segmentation (agency-level separation, multi-tenant controls)
• Incident response maturity

For fintech infrastructure teams, this is the reminder: security isn’t an appendix to the pitch deck—security is the feature.

What a SmartPay pilot would need to prove (to be credible)

A pilot only matters if it demonstrates measurable outcomes without adding operational burden. Here are the metrics I’d watch—whether you’re evaluating Ramp, another expense platform, or building similar capabilities internally.

1) Reduction in out-of-policy spend (not just detection)

Detection is table stakes. The more important metric is prevention:

• Percent of transactions blocked or re-routed before settlement
• Reduction in policy exceptions over time
• Decrease in after-the-fact corrections and journal reclasses

2) Time-to-close and reconciliation workload

A practical benchmark question: Did month-end close become faster?

For a pilot, you’d want to quantify:

• Hours spent on receipt chasing and coding
• Percentage of transactions auto-coded correctly
• Reduction in manual approval touches

3) Audit readiness and evidence quality

A system can be “compliant” and still fail an audit because evidence is incomplete or hard to retrieve.

A credible pilot should prove:

• Approval chains are complete and immutable
• Policy versions are tracked over time
• Reporting matches what auditors ask for (not what dashboards look like)

4) Fraud outcomes with low disruption

This is where many pilots stumble. If the fraud model blocks legitimate spend, users route around controls.

Look for:

• Fraud loss rate (or incident rate) compared to baseline
• False positive rate and resolution time
• Step-up approval success rate (how often it prevents misuse without blocking work)

Snippet-worthy line: “At government scale, the best fraud system is the one that stops abuse without teaching everyone how to bypass it.”

Practical lessons for fintech and payments leaders (even if you’ll never sell to government)

You don’t need a government customer to benefit from government-grade infrastructure thinking. SmartPay-level constraints are a forcing function that makes payment systems better everywhere.

Build controls as code, not as policy PDFs

If your expense rules live in documents and tribal knowledge, AI won’t save you. Convert policies into enforceable controls:

• Merchant category controls (allow/deny lists)
• Per-role spend limits
• Time, location, and vendor constraints
• Approval routing rules tied to risk scores

AI can then assist by identifying which rules are outdated, which thresholds create noise, and where exceptions cluster.

Treat AI as a risk team member, not a UI feature

The most valuable AI in expense management is often invisible:

• Auto-matching receipts and extracting key fields
• Flagging anomalies with short explanations
• Predicting coding fields based on prior decisions

If your AI feature only works when someone clicks a button, adoption will lag. If it quietly removes work, people will defend it.

Instrument everything you’ll need for disputes and audits

For payment infrastructure, observability isn’t just latency charts. It’s business evidence:

• Decision logs (why approved/declined)
• Model versions and rule versions
• Who changed limits, when, and under which authority

This is also how you de-risk AI: you can roll back, compare cohorts, and prove impact.

People Also Ask (answered directly)

Will AI replace auditors or finance teams in expense programs? No. AI reduces manual review and improves detection, but audit and finance teams still define policy, investigate edge cases, and own accountability.

Is government adoption of fintech realistic given procurement friction? Yes, but only when the product is built for controls, evidence, and security from day one. Procurement friction is real; weak governance makes it worse.

What’s the biggest mistake in AI expense management rollouts? Optimizing for pretty categorization instead of enforceable controls. If you can’t prevent bad spend, you’re just producing nicer reports.

The bigger trend: AI is becoming the operating system for spend

Ramp being considered for a SmartPay charge card pilot program is a small headline with a big implication: buyers are starting to expect AI-driven controls as part of payment infrastructure, not as an add-on. The organizations that win the next phase of expense management will be the ones that combine real-time payments decisioning, strong governance, and audit-ready evidence.

For teams working in AI in payments, this is the challenge I’d put on the table for 2026 planning: can your infrastructure prove, in numbers, that it reduces fraud and waste without increasing friction? If the answer is fuzzy, your competitors will make it crisp.

If you’re modernizing expense programs or building fintech infrastructure that touches high-volume card spend, now’s a good time to pressure-test your stack against government-grade expectations: pre-spend controls, explainability, and operational resilience.

What would your current system do if it had to explain every decline and every exception to an auditor—six months from now, with receipts missing and staff rotated out?