Fintech Contracts Need Transparency—AI Can Enforce It

A $25 million government contract is big enough to change a fintech’s growth curve—and political scrutiny is almost guaranteed. That’s why the news that a U.S. congressman is investigating whether expense-management fintech Ramp received preferential treatment in a federal procurement process should land as more than foreign politics. It’s a real-world reminder of what happens when financial operations, contract awards, and oversight depend too much on “trust me” processes.

For Ghana’s fintech ecosystem—where mobile money is the everyday rails for payments and where more institutions are digitising procurement, reimbursements, and supplier payments—this is not distant drama. It’s a cautionary tale. The moment money moves faster than controls, confidence becomes the product, and a single procurement controversy can undermine it.

This post sits inside our series “AI ne Fintech: Sɛnea Akɔntabuo ne Mobile Money Rehyɛ Ghana den” and uses the Ramp story as a practical case study: what transparency failures look like, why they keep happening, and how AI-driven automation (done right) makes preferential treatment harder to hide.

What the Ramp investigation signals (and why it matters)

Answer first: The Ramp story signals that fintech credibility isn’t only about product features—it's about process integrity, especially when contracts, public funds, and fast growth collide.

Based on the RSS summary, Rep. Gerald Connolly (ranking member of the U.S. House Oversight Committee) has initiated an investigation into whether Ramp received preferential treatment in its attempt to win a $25M federal contract, requesting documents and details from the General Services Administration (GSA).

Two things are worth paying attention to, even from the limited summary:

1. The allegation is procedural, not technical. Nobody needs to prove the software works or doesn’t work to raise red flags. The question is whether the process was fair.
2. Public procurement is a trust amplifier. If the process is clean, the winning vendor gains legitimacy fast. If the process looks “massaged,” everyone loses—agency leadership, the vendor, and the broader tech ecosystem.

In Ghana, the parallel is clear: as more payments and accounting move into mobile money workflows, the next trust battles won’t be about whether MoMo works. They’ll be about whether digital spending, supplier onboarding, and contract awards are transparent, auditable, and consistent.

Preferential treatment usually hides in boring admin

Answer first: Preferential treatment rarely looks like a movie bribe; it typically shows up as exceptions, vague criteria, undocumented meetings, and rushed approvals.

I’ve found that when controversies hit, the root cause isn’t “too much innovation.” It’s too many manual steps and too much discretionary power without strong records.

Where procurement and expense processes break

Here are common fault lines—whether you’re awarding a government contract or approving corporate spend:

• Unclear evaluation criteria: If scoring rules aren’t explicit, scoring becomes “flexible.”
• Unequal access to information: One bidder gets more guidance, earlier timelines, or inside context.
• Exception-based approvals: “We waived this requirement just this once.” Those “onces” add up.
• Poor audit trails: Decisions happen on calls, WhatsApp, hallway chats—then vanish.
• Vendor onboarding gaps: Beneficial ownership checks and conflict-of-interest declarations are incomplete.

This matters for Ghana because these are the same cracks that can appear in:

• institutional mobile money disbursements (stipends, per diems, field allowances),
• SME expense claims and reimbursements,
• supplier payments linked to digital invoices,
• grant programs that pay beneficiaries through MoMo.

If people can’t reconstruct why a payment or award happened, they assume the worst.

One-liner you can quote: “If you can’t explain a financial decision from the data trail, you don’t have governance—you have hope.”

Where AI actually helps (and where it doesn’t)

Answer first: AI helps most when it’s used to standardise decisions, enforce policy, and surface anomalies, not when it replaces accountability.

AI in fintech is often marketed like magic. I don’t buy that. AI is valuable because it’s consistent, fast, and good at pattern detection. But it must be tied to hard controls.

1) AI for objective, repeatable scoring

If a procurement team uses a scoring model, AI can enforce that:

• all bids are scored using the same rubric,
• scoring weights are version-controlled,
• reviewers can’t change criteria midstream without a logged justification.

This directly addresses “preferential treatment” risk: you can still make a biased decision, but you can’t do it quietly.

2) AI for anomaly detection in expenses and payments

Expense platforms (and internal finance teams) can use AI to flag:

• duplicate invoices across vendors,
• split purchases designed to bypass approval limits,
• unusual reimbursement timing (e.g., always after hours),
• repeated exceptions by the same approver or department,
• vendor bank account changes that don’t match historical patterns.

In Ghana’s context, apply the same logic to mobile money payments:

• repeated payouts to numbers registered to similar identity attributes,
• suspicious clustering (many new recipients paid within minutes),
• round-number payments that don’t match program rules,
• recipient SIM changes right before payouts.

3) AI for compliance-by-design (policy enforcement)

The strongest use case is not “AI that predicts fraud.” It’s AI that prevents policy breaches before money leaves.

Examples:

• blocking spend that exceeds budget lines,
• requiring 2+ approvals for certain categories,
• forcing attachment of receipts/invoices and validating them,
• verifying vendor registration and tax status before payment.

Where AI doesn’t help

AI won’t fix:

• leadership that tolerates exceptions,
• procurement criteria written to fit a preferred vendor,
• off-platform influence (calls, informal pressure).

That’s why the operating model matters as much as the model accuracy.

What Ghana’s fintech and mobile money operators should learn from this

Answer first: Ghana’s fintech growth needs auditability as a product feature—especially for institutions using mobile money at scale.

Ghana has one of the most vibrant mobile money environments in Africa, and the market keeps pushing into new use cases: payroll-like disbursements, merchant payments, micro-insurance, lending collections, and agent networks. As these flows scale, scrutiny rises from regulators, boards, auditors, donors, and the public.

A practical checklist for “AI + accountability” in financial operations

If you’re building or buying an AI-driven fintech tool for accounting, expenses, or disbursements, insist on these controls:

1. Immutable audit trails
   • Every approval, override, and policy exception must be timestamped and attributable.
2. Role-based access and segregation of duties
   • The person who creates a vendor shouldn’t be the person who approves payment.
3. Policy rules that are visible and testable
   • If a policy can’t be simulated (“Would this transaction pass?”), it can’t be trusted.
4. Explainable AI outputs
   • Flags must come with reasons humans can verify (e.g., “duplicate invoice number,” “vendor bank changed”).
5. Independent review workflows
   • High-risk transactions should be routed to a different approver automatically.
6. Data quality gates
   • AI is only as good as the inputs: enforce required fields, validated IDs, consistent merchant categories.

“People Also Ask” (real questions teams raise)

Is automation enough to prevent preferential treatment? Automation reduces the surface area for manipulation, but governance finishes the job. The win is traceability: when decisions are logged, exceptions become visible.

What’s the best starting point for SMEs using mobile money? Start with expense policy automation: approval limits, receipt capture, category controls, and basic anomaly flags. Then expand to vendor management and contract workflows.

Will AI increase bias in procurement? It can—if your training data reflects old patterns. That’s why you need rule-based guardrails, human review for edge cases, and periodic fairness checks on outcomes.

A better way to approach fintech trust: “show your work”

Answer first: The fastest route to trust in fintech is simple: systems that can show their work—every time.

The Ramp investigation is still just that—an investigation. But the lesson is already useful. When contract awards and financial approvals rely on discretion without strong records, someone will eventually question the legitimacy of the outcome.

For Ghana’s fintech leaders building around mobile money, and for finance teams modernising akɔntabuo (accounting) with AI tools, the standard should be higher than “we didn’t do anything wrong.” Aim for “we can prove we did everything right.”

If your organisation is scaling MoMo disbursements, automating expenses, or digitising supplier payments, build the control layer now—before the first headline forces you to.

Forward-looking question: If an auditor asked you to replay every approval and exception behind your last 1,000 payments, could your systems do it in minutes—or would it take weeks of phone calls and spreadsheets?