AI Diligence // Payments & fintech # The model approves the payment. And owns the loss. Fraud scoring, credit decisioning, KYC and AML, dispute automation. In payments the AI sits directly on the money — every model decision is an authorization, a loss, or a regulatory finding. And the take rate you're underwriting rests on models no one in the deal team has ever opened. Book a scoping call → What typically breaks ↓ The confirmatory assessment — the actual product, not a mockup. 01 // The four surfaces ## Four surfaces, read for this sector. The method doesn't change. What changes is where the risk concentrates — and in payments & fintech, it concentrates hard. Operational — why it matters Fraud and AML models are the line between a loss provision and a consent order . Both have a number, and both land on you. Product — why it matters If risk decisioning is what they sell, the model is the IP — and it may turn out to be a vendor's model with a wrapper on it. Market — why it matters Agentic commerce is changing who initiates a payment. If an agent chooses the rails, checkout placement stops being an asset. Financial — why it matters Loss rate, take rate, reserves. A fraud model drifting 40bps is an EBITDA event , not an engineering ticket — and the reserve was set before it drifted. 02 // The failure modes ## What typically breaks in payments & fintech. These are the findings we look for first — ranked by how often they turn up, and what they hit when they do. Frequency reflects what we look for and how often it surfaces in this sector — not a published benchmark. Sector baselines are built from completed engagements and released only when the sample supports them. 03 // One finding, priced ## A finding is only real when it has a number. The finding Value at risk What it becomes Fraud model last retrained 19 months ago, with loss rates already running 30bps above plan $7.3M Reserve true-up · price reduction · escrow Why it matters The reserve and the take rate both assume a model that has stopped working Illustrative finding · representative of this sector, not a specific client engagement. 04 // How you buy it ## Screen fast. Then go deep. Pre-LOI ### Flash Screen A go/no-go read before the diligence budget commits. ≈ 10 SME hours included - Composite score and red-flag triggers - Sector failure modes checked first - Screen memo: proceed, price, or pass Confirmatory ### Full Assessment The IC-defensible read, sized to your window. ≈ 70 SME hours included - All eight weighted domains, evidence-linked - Management and technical interviews - Findings → terms working session - IC readout and 100-day plan input Sector Healthcare services → Sector Software / SaaS → Sector Insurance & financial services → Sector Industrials & distribution → Sector Business & professional services → Sector AI infrastructure & specialty industrials → Sector Payments & fintech → ## Don't sign for AI you haven't seen. Bring us the target. We'll score the AI behind it — with the payments & fintech failure modes checked first — and hand your deal team the price, the terms, or the walk. Book a scoping call → Axiom · AI Diligence — illustrative figures throughout Industries Portfolio deals@axiomdiligence.ai