Fintech Case Interview: Payments, Neobanks, Lending & Crypto Strategy (2026)

Most candidates preparing for financial services cases study branch consolidation and loan portfolio management. The firms have moved on. Global fintech revenue hit $395 billion in 2025, growing at 18.2% annually, and BCG projects the sector will reach $1.5 trillion by 2030. McKinsey, BCG, and Bain each have dedicated fintech practices generating case interview prompts around digital payments, neobank growth strategy, lending platform profitability, and crypto infrastructure. If you interview for a financial services or technology practice in 2026, at least one of your cases will involve fintech economics.

The 4 Fintech Case Types You Will Face

Fintech is broad. In case interviews, the prompts cluster into four categories, each requiring different frameworks and knowledge.

Each type demands specific vocabulary and numerical intuition. The rest of this guide breaks down frameworks and worked examples for each.

Digital Payments: Framework and Unit Economics

Payments is the largest fintech vertical — $135 billion in equity invested over 25 years, representing nearly a quarter of all global fintech funding. BCG projects global payments revenue will hit $2.4 trillion by 2029. When an interviewer gives you a payments case, they expect you to understand how money actually moves.

The payments value chain:

Every card transaction involves four parties: the cardholder, the merchant, the issuing bank (cardholder's bank), and the acquiring bank (merchant's bank). The payment processor (Stripe, Adyen, Square) sits between the merchant and the acquiring bank.

Key economics:

• Merchant discount rate (MDR): The total fee the merchant pays per transaction, typically 1.5-3.5% of transaction value
• Interchange fee: The largest component of MDR, paid by the acquirer to the issuer, typically 1.0-2.0% for credit cards and 0.2-0.5% for debit
• Network fee: Paid to Visa/Mastercard, typically 0.1-0.15%
• Processor margin: MDR minus interchange minus network fee — often only 0.3-0.8% of transaction value

"Small margins, massive volume" is the defining characteristic. A payment processor earning 0.5% margin on $200 billion in total payment volume (TPV) generates $1 billion in revenue.

Framework for a payments case:

1. Market sizing: Total addressable market by transaction volume and value. Segment by geography, merchant type, and payment method.
2. Revenue model: Take rate (processor margin) times TPV. Model revenue by merchant segment — SMBs pay higher MDR (2.5-3.5%) than enterprises (1.5-2.0%).
3. Cost structure: Technology infrastructure, compliance and licensing, fraud losses (0.05-0.1% of TPV), merchant acquisition costs.
4. Competitive dynamics: Switching costs, network effects, regulatory barriers (PSD2 in Europe opens the market to new entrants).
5. Growth levers: Geographic expansion, new payment methods (BNPL, real-time payments), value-added services (analytics, lending).

Neobank Growth: From User Acquisition to Profitability

Neobank cases test whether you understand the full growth funnel — not just "how to get users" but how to convert free users into profitable customers. This matters because most neobanks are still unprofitable. The standard path to profitability requires moving beyond free debit cards into lending, premium subscriptions, or interchange revenue at scale.

Neobank unit economics (typical ranges):

• Customer acquisition cost (CAC): $5-50 depending on channel and geography
• Activation rate (user actually deposits funds): 30-60% of signups
• Average revenue per user (ARPU): $30-80/year for basic accounts, $150-300/year for premium
• Net interest margin on deposits: 2-4%
• Interchange revenue per active user: $20-40/year (based on average spend of $8,000-15,000/year)

The profitability math:

For a neobank with 2 million users, a $20 CAC, 40% activation rate, and $50 ARPU:

• Active users: 2M x 40% = 800,000
• Revenue: 800K x $50 = $40M
• Acquisition costs (sunk): 2M x $20 = $40M
• Annual operating costs at scale: $60-80M (engineering, compliance, customer support, infrastructure)
• Gap to profitability: $20-40M/year

The path to close this gap: increase activation rate (40% to 55% = +$7.5M revenue), launch lending products ($150 ARPU on 10% of users = +$12M), and grow interchange through higher spend per user.

For a deeper dive on profitability decomposition, see the profitability framework.

Lending Platform Profitability: Risk-Adjusted Thinking

Lending cases require you to think about risk in a way that other case types do not. A lending platform's revenue is straightforward (interest income + origination fees), but profitability depends entirely on default rates — and default rates are easy to get wrong.

Key lending metrics:

• Net interest margin (NIM): Interest earned on loans minus cost of funds, typically 2-6% for digital lenders
• Default rate: Percentage of loans that go unpaid, typically 2-5% for prime borrowers, 8-15% for subprime
• Loan-to-value (LTV) ratio: For secured lending, the loan amount divided by the collateral value
• Cost of acquisition per funded loan: $200-800 depending on channel and loan type

Framework for a lending case:

1. Revenue model: Loan volume x average interest rate + origination fees (1-5% of loan value)
2. Credit risk: Default rate x average loan size x loss given default (LGD). For unsecured personal loans, LGD is typically 60-80%.
3. Unit economics per loan: Interest income minus cost of funds minus expected loss minus servicing cost
4. Portfolio-level profitability: Unit economics x number of loans minus fixed costs (technology, compliance, collections)
5. Regulatory constraints: Capital requirements, lending caps, consumer protection rules that limit APR

Worked Case: Neobank Profitability Turnaround

Prompt: "A European neobank has 3.2 million registered users and is burning $55 million per year. The CEO wants to reach breakeven within 18 months without raising another funding round. How would you approach this?"

Structure: "I'd analyze this across three dimensions: revenue optimization — can we extract more value from existing users? Cost reduction — where is the $55M being spent and what is discretionary? And user economics — what does the distribution of user value look like, and are we over-investing in low-value users?"

Key analysis (with numbers):

The interviewer shares: 3.2M registered users, but only 1.1M are active (34% activation). ARPU on active users is $42/year. Total revenue is $46.2M. Cost breakdown: $28M technology and infrastructure, $12M customer acquisition, $8M compliance and licensing, $7M customer support.

• Revenue: 1.1M x $42 = $46.2M
• Costs: $28M + $12M + $8M + $7M = $55M
• Loss: $8.8M/year (less than expected — closer to breakeven than the "burning $55M" framing suggests, since $55M is total costs, not losses)
• The real gap is $8.8M, not $55M

"First correction: the burn rate framing is misleading. The company is losing $8.8M on $46.2M in revenue — an 81% gross margin business that is 19% away from breakeven. This changes the strategy entirely. We do not need a dramatic pivot — we need targeted improvements."

Three levers to close the $8.8M gap:

1. Increase activation rate from 34% to 45%: That adds 352K active users x $42 ARPU = +$14.8M revenue. Achievable through better onboarding (push notifications for first deposit, simplified KYC).
2. Cut CAC spend by 40%: Reduce from $12M to $7.2M = $4.8M saved. Shift from paid channels to referral programs that have 3x better conversion rates at 60% lower cost per acquisition.
3. Launch a premium tier at $8/month: If 5% of active users convert, that is 55K x $96/year = +$5.3M revenue.

Any two of these three levers closes the gap. All three creates a $16M buffer.

Recommendation: "The neobank is 19% from breakeven, not in crisis. I'd prioritize activation rate improvement — it is the highest-ROI lever because it monetizes users already acquired. Pair that with CAC reduction. These two alone should reach breakeven within 12 months, ahead of the 18-month target. The premium tier is a medium-term revenue diversification play."

Worked Case: Crypto Strategy for a Traditional Bank

Prompt: "A mid-size US bank ($45B in assets) is considering launching a stablecoin for B2B cross-border payments. Their CEO believes this could reduce correspondent banking costs by 60%. Should they proceed?"

Structure: "I'd evaluate this across four areas: market opportunity — how large is the B2B cross-border payment flow they could capture? Cost advantage — does a stablecoin actually deliver the 60% cost reduction the CEO claims? Regulatory risk — what is the licensing and compliance landscape? And competitive positioning — are they too late, too early, or well-timed?"

Key analysis:

The interviewer shares: the bank processes $8B annually in cross-border B2B payments. Current correspondent banking costs average 1.5% of transaction value ($120M/year). Stablecoin settlement costs are estimated at 0.3-0.5% of value.

• Current cross-border costs: $8B x 1.5% = $120M/year
• Stablecoin estimated costs: $8B x 0.4% = $32M/year
• Potential savings: $120M - $32M = $88M/year (73% reduction, exceeding the CEO's 60% claim)
• Build cost for stablecoin infrastructure: estimated $15-25M
• Payback period: $20M build / $88M annual savings = under 3 months (unrealistically fast — suggests hidden costs)

"The raw math looks compelling, but I am skeptical of the 3-month payback. Hidden costs include regulatory licensing ($5-10M and 12-18 months), integration with existing core banking systems ($10-20M), counterparty onboarding (clients must adopt the stablecoin), and ongoing compliance monitoring. A more realistic timeline is 2-3 years to breakeven."

BCG's 2025 Global Payments Report notes that stablecoin-based B2B payments grew 30x in two years, but real-world payments still account for only 1% of total stablecoin transaction volume. This means the infrastructure is maturing but adoption is early.

Recommendation: "Proceed, but as a pilot — not a full launch. Start with 3-5 of the bank's largest cross-border corridors (representing $2B of the $8B volume), build on an existing stablecoin protocol rather than creating a proprietary one, and target $22M in annual savings on the pilot corridors. Use the pilot to prove the cost model and navigate the regulatory process before scaling. The strategic risk of not moving is that fintech competitors capture this market while the bank waits."

Fintech-Specific Frameworks: Beyond the Basics

Standard case interview frameworks work as starting points, but fintech cases require additional analytical layers.

When to use which layer:

• Payments cases: Heavy on Layer 1 (economics) and Layer 4 (platform dynamics)
• Neobank cases: Heavy on Layer 2 (funnel) and Layer 1 (unit economics)
• Lending cases: Heavy on Layer 1 (risk-adjusted economics) and Layer 3 (regulatory)
• Crypto cases: Heavy on Layer 3 (regulatory) and Layer 5 (build vs. partner)

Based on 200+ fintech practice sessions on Road to Offer, candidates who apply a generic profitability framework to a fintech case score 30-40% lower than those who incorporate the digital-specific layers above. The difference is specificity — interviewers can immediately tell whether you understand how a payment processor makes money or whether you are guessing.

For technology-related case frameworks, see also the digital transformation case interview guide.

Key Fintech Metrics: The Numbers You Must Know

Walk into a fintech case knowing these ranges. Interviewers expect you to sanity-check data they provide — and unrealistic assumptions signal weak preparation.

For additional practice with financial calculations in case interviews, see case interview math practice.

Common Fintech Case Mistakes

Mistake 1: Ignoring regulatory constraints. A lending platform cannot simply "enter a new market" without licensing. A crypto product cannot launch without compliance infrastructure. If your recommendation ignores regulatory feasibility, it is incomplete. Always include a "regulatory requirements" bucket in your structure.

Mistake 2: Confusing revenue with margin in payments. The MDR is not the processor's revenue. The processor's margin is the MDR minus interchange minus network fees. A 2.9% MDR might yield only 0.5% margin — getting this wrong ruins your entire profitability analysis.

Mistake 3: Treating all users as equal in neobank cases. In most neobanks, the top 10-15% of users generate 60-70% of revenue. Recommendations that treat all 3 million users identically miss the power-law distribution that drives the business.

Mistake 4: Assuming fintech = no regulation. Fintech companies are heavily regulated — often more scrutinized than incumbents because regulators view them as novel risks. PSD2 in Europe, state-by-state lending licenses in the US, and evolving crypto regulation globally all shape what fintech companies can and cannot do.

Mistake 5: Skipping the "why now?" question. Fintech cases often hinge on timing — a regulatory change (open banking mandates), a technology shift (real-time payment rails), or a market disruption (pandemic-driven digital adoption). If you do not ask what changed to create the opportunity, your analysis lacks grounding.

Test Your Knowledge

Related Guides

• Case interview frameworks complete guide — foundational structures that adapt to fintech cases
• Profitability framework — the financial decomposition skills underlying all fintech unit economics
• Digital transformation case interview — build-vs-buy frameworks and technology strategy cases
• Market entry framework — essential for fintech geographic expansion and new product launch cases
• Case interview math practice — sharpen the mental math speed that fintech cases demand
• Consulting career path — understanding fintech practice areas across firms to target your applications

Sources and Further Reading (checked April 8, 2026)

• BCG, Global Fintech Report 2025 — Fintech's Next Chapter: https://www.bcg.com/publications/2025/fintechs-scaled-winners-emerging-disruptors
• BCG, Global Payments Report 2025: https://www.bcg.com/publications/2025/global-payments-transformation-amid-instability
• Fortune Business Insights, Fintech Market Size 2025-2034: https://www.fortunebusinessinsights.com/fintech-market-108641
• McKinsey, Blockchain and Digital Assets: https://www.mckinsey.com/industries/financial-services/our-insights/blockchain-and-digital-assets
• Hacking the Case Interview, Financial Services Case Interview: https://www.hackingthecaseinterview.com/pages/financial-services-case-interview
• DigitalDefynd, Top 25 Fintech Case Studies: https://digitaldefynd.com/IQ/fintech-case-studies/