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Investing in fintech?
Talk to a partner who has run payment systems, not just reviewed them.
Industries
Financial Services and Fintech
Fintech diligence led by people who ran payments at scale
Diligence across payments, lending, insurance, and wealth platforms.
In financial technology, a scaling failure isn’t slow pages. It’s a double charge, a lost transaction, or an audit finding that halts growth. AKF’s founding partners led technology at eBay and PayPal, and we bring that operating history to every fintech assessment. We look past the demo to how money actually moves through the system.
What we find most often in fintech deals
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The database is the first ceiling.
One shared database with no read/write separation or partitioning handles today’s volume but hasn’t been tested against the combined load after an acquisition or the arrival of a large bank customer. We lay out the path on the AKF Scale Cube: read replicas first (X-axis), then partitioning by customer or product (Z-axis).
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Disaster recovery that has never been tested.
A single cloud region, no documented recovery time or data-loss targets, and cross-region recovery that would take days rather than hours. Larger financial institutions ask about this early. We also look for fault isolation (AKF’s swim lanes) so a failure in reporting or marketing services can’t stop money moving.
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Tenant isolation the largest customers will require.
Multi-tenant platforms that need a clear way to give big banks dedicated environments, or isolation rules that vary by customer and complicate compliance and onboarding.
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Dependencies that don’t transfer at close.
Security tooling, scanning, or incident-response support provided by a parent company, which disappears in a carve-out unless a transition services agreement covers it. Also, operations that depend heavily on a single CRM or processor.
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Strong engineering that isn’t a moat.
Security and architecture are often better than expected, but a well-funded competitor could rebuild the same functionality. We assess what is actually defensible: proprietary data, embedded workflows, and integrations.
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Models making regulated decisions without governance.
Underwriting or credit models with no formal versioning, monitoring, or retraining, and no link between model output and loss ratios or unit economics.