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BNPL vs. credit card economics comparator

Models a side-by-side unit economics comparison between Buy Now Pay Later and revolving credit card products for a defined customer/merchant scenario — producing net revenue per transaction, risk-adjusted margin…

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Purpose

Produce a defensible, transaction-level economics comparison between a BNPL product and a revolving credit card for a specific customer and merchant scenario — so a lending or payments leader can see net revenue per transaction, risk-adjusted margin, lifetime value differential, and funding cost sensitivity side by side, and decide which product to fund or prioritize.

When to Use

Invoke this skill when the user asks to:

  • Compare the profitability of a BNPL product against a revolving credit card line
  • Decide whether to launch or fund a BNPL offering versus expanding card lending
  • Model unit economics for a specific merchant category (electronics, fashion, travel, home goods)
  • Stress-test how rising funding costs change the BNPL-vs-card decision
  • Quantify the lifetime value tradeoff between a short-term installment customer and a revolving cardholder
  • Build a board- or investment-committee-ready economics case for a product bet

Inputs Required

Before starting, collect:

  • Scenario name: Customer segment and merchant category being modeled (e.g., "Prime borrower, $400 electronics purchase")
  • Average transaction value (AOV): Typical purchase amount being financed
  • Merchant discount rate (MDR): Fee charged to the merchant for BNPL (often 4–6%); and interchange + card MDR for the card path (often 1.5–3%)
  • Card APR and revolve rate: Card interest rate, and the share of cardholders carrying a balance month over month
  • Expected loss rates: Default / charge-off rate for each product (BNPL net loss often 1–3%; card charge-off often 3–6%)
  • Funding cost: Cost of capital / wholesale funding rate to finance receivables (annualized)
  • Customer horizon: Expected relationship length and repeat-purchase frequency for LTV modeling

If revolve rate, loss rates, or repeat frequency are missing, apply published industry midpoints and flag each assumption explicitly in the output.

Instructions

Step 1: Establish the Modeling Frame

Lock the scenario before computing. State the AOV, merchant category, customer segment, and the two products being compared. Define the BNPL structure (typical: Pay-in-4, ~6-week duration, 0% to consumer, revenue from MDR + late fees) and the card structure (revenue from interchange + interest on revolving balances). Note the funding cost as the single most important shared variable. List every assumption (revolve rate, loss rate, repeat frequency, late-fee incidence) in a visible assumptions block so the numbers are auditable.

Step 2: Compute Net Revenue Per Transaction — BNPL Path

Build the per-transaction revenue stack:

  1. Gross merchant revenue = AOV × BNPL MDR
  2. Late/penalty fee revenue = AOV × late-incidence rate × average fee (model conservatively; many regulators cap this)
  3. Less: expected credit loss = AOV × BNPL net loss rate
  4. Less: funding cost = AOV × funding rate × (BNPL duration in years, typically ~0.115 for 6 weeks)
  5. Less: processing/servicing cost = a fixed per-transaction operating cost Report Net revenue per BNPL transaction in both dollars and as a % of AOV.

Step 3: Compute Net Revenue Per Transaction — Card Path

Build the per-transaction revenue stack on a comparable horizon:

  1. Interchange revenue = AOV × interchange rate
  2. Interest revenue = AOV × revolve rate × APR × average months carried (convert to a per-transaction-equivalent for a single purchase, then note the annualized figure separately)
  3. Less: expected credit loss = balance × card charge-off rate
  4. Less: funding cost = carried balance × funding rate × time carried
  5. Less: rewards/servicing cost = rewards redemption + servicing per transaction Report Net revenue per card transaction in dollars and as % of AOV. Make explicit that the card path earns over a longer time horizon than the ~6-week BNPL cycle, and normalize the comparison (per-transaction and annualized).

Step 4: Risk-Adjusted Margin

For each product, compute margin after credit and funding risk:

  • Risk-adjusted margin % = (net revenue − expected loss − funding cost) ÷ AOV (or ÷ exposure for card)
  • Show the loss-and-funding drag as a discrete line — how many margin points each consumes
  • Identify which product is more sensitive to a deterioration in credit (loss rate stress) versus a rise in rates (funding stress)

Step 5: Customer Lifetime Value Differential

Move from one transaction to the relationship:

  • BNPL LTV = net revenue per transaction × annual transaction frequency × expected relationship years (BNPL drives high frequency, low per-transaction margin, lower switching cost)
  • Card LTV = net revenue per cardholder × relationship years (cards earn recurring interest and interchange across all spend, higher per-customer margin, stickier but higher acquisition and rewards cost)
  • Report the LTV differential (card LTV − BNPL LTV) in dollars and as a ratio, and name the strategic implication: BNPL as an acquisition/frequency engine vs. card as a margin/retention engine.

Step 6: Funding Cost Sensitivity Table

Build a sensitivity grid showing how net economics shift as funding cost rises. Run funding rate across at least 5 steps (e.g., 3%, 5%, 7%, 9%, 11%) and show, for each:

  • BNPL net revenue per transaction
  • Card net revenue per transaction
  • The crossover point (if any) where one product overtakes the other State the headline finding: BNPL's short duration insulates it from rate rises far more than the card's carried balance — quantify the divergence.

Step 7: Verdict and Conditions

Deliver an opinionated recommendation tied to the scenario: which product wins on per-transaction margin, which wins on LTV, and under what funding regime the answer flips. State the one or two conditions that would change the call.

Output Format

  • Structure: Assumptions block first, then a two-column comparison for each economic dimension, then the sensitivity table, then verdict
  • Length: 900–1,300 words plus tables
  • Format: Structured document with labeled dollar figures, percentages, and at least two tables (a per-dimension comparison table and the funding sensitivity grid)
  • Sections: Modeling Frame & Assumptions → BNPL Net Revenue → Card Net Revenue → Risk-Adjusted Margin → LTV Differential → Funding Cost Sensitivity → Verdict & Conditions
  • Tone: Lending economics director — quantitative, auditable, no hedging; every number traces to a stated assumption

Quality Checklist

Before delivering output, verify:

  • [ ] Every figure traces to a line in the assumptions block — no unsourced numbers
  • [ ] Net revenue per transaction is reported for both products in dollars AND as % of AOV
  • [ ] The comparison is normalized for the duration mismatch (~6-week BNPL vs. revolving card horizon) — not an apples-to-oranges per-transaction figure
  • [ ] The funding sensitivity table runs at least 5 funding-rate steps and identifies any crossover point
  • [ ] Risk-adjusted margin isolates loss drag and funding drag as separate line items
  • [ ] The verdict names the funding regime under which the recommendation reverses

Notes

  • BNPL's economic advantage is duration: receivables turn over in ~6 weeks, so funding cost barely scratches per-transaction margin — this is why BNPL holds up in rising-rate environments while card net interest margin compresses.
  • Card economics live or die on the revolve rate. If the modeled segment is transactor-heavy (pays in full), card revenue collapses to interchange and BNPL often wins outright.
  • Treat late-fee revenue conservatively — regulatory pressure (e.g., consumer-protection caps) can remove it as a line item, and a model that depends on late fees for BNPL profitability is fragile.
  • When the user gives a single funding rate, still run the sensitivity grid — the rate environment is the variable most likely to invalidate a static comparison.

How to use this skill

This file is a Claude Code skill in Anthropic's standard format — markdown with YAML frontmatter at the top.

Installing in Claude Code:

  • ~/.claude/skills/<folder-name>/SKILL.md — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)
  • <your-project>/.claude/skills/<folder-name>/SKILL.md — auto-loads only when working in that project (recommended for project-specific skills)

Restart Claude Code or open a new session to register.

Using in Claude.ai, ChatGPT, Gemini, or other AI tools:

  1. Drag this file into a new chat, or copy and paste its contents
  2. Send your task or request as your next message — the AI will apply the skill where it fits

For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills