Lecture 7: Fintech Business Models

Neobanks, embedded finance, BNPL, Open Banking, Big Tech in finance

Prof. Dr. Andre Guettler Director of the Institute
Helmholtzstraße 22, Room 205
andre.guettler@uni-ulm.de
+49 731 50 31 030

Oliver Padmaperuma Doctoral Candidate
Helmholtzstraße 22, Room 203
oliver.padmaperuma@uni-ulm.de
+49 731 50 31 036

7.1 Course objectives

• 7.1 Course objectives
• 7.2 Recap from Lecture 6
• 7.3 Neobanks
• 7.4 Embedded finance & BaaS
• 7.5 BNPL
• 7.6 Open Banking & PSD2
• 7.7 Big Tech in finance
• 7.8 Conclusion of Lecture 7

• Welcome to
• Course Objective
• Course at a glance (1/3)
• Course at a glance (2/3)
• Course at a glance (3/3)
• Assignments / Exams

Welcome to Emerging Technology & Finance

• This is a flipped-classroom Bachelor course: every regular lecture (odd weeks) is followed by a flipped session (even weeks) where all groups present on the same topic. There is no exam to register for — sign up on the course Moodle page by 15 October 2026 so you receive announcements and the token-allocation quiz links.
• Form a group of 4 by the end of Week 1 (Moodle sign-up sheet). Stragglers will be allocated by the lecturers.
• Grading is 100% cumulative across the 6 flipped sessions: each session = 50% peer-allocated tokens + 50% lecturer evaluation. Each group gets 20 fresh tokens every flipped week to allocate to other groups via a Moodle quiz within 5 minutes of the session ending.
• Submission per session: upload your slide PDF to Moodle before each flipped session starts. Ask questions during or right after each session — that is the preferred channel.
• Admin / studies / exam-eligibility questions go to the registrar’s office (Studiensekretariat) at studiensekretariat@uni-ulm.de.
• Course-content questions outside class: email oliver.padmaperuma@uni-ulm.de, CC andre.guettler@uni-ulm.de.
• We also recommend the student advisory service.

Course Objective

Scope

We will:

• Survey six emerging-technology modules at the cutting edge of finance: agentic AI · blockchain & DeFi · fintech business models · RegTech & cybersecurity · CBDCs
• Pair every regular lecture with a flipped session in which every group presents their angle on the topic
• Train critical evaluation, presentation, and peer-judgment skills via a transparent token-based peer-grading mechanic
• Place the technologies in a real-world business and regulatory context (PSD2/3, MiCA, EU AI Act, post-quantum standards)

We will NOT:

• Build production-grade fintech systems or trade live capital
• Cover deep technical implementations (we treat code as supplement, not core)
• Run a separate written exam or final-pitch competition — the cumulative flipped-session grade is the entire grade

Approach

Flipped-classroom alternation (12 weeks)

• Odd weeks (W1, W3, W5, W7, W9, W11): regular lecture introducing the topic
• Even weeks (W2, W4, W6, W8, W10, W12): flipped session — all groups present and allocate tokens
• Groups of 4, formed by end of Week 1

Token mechanic (the grading vehicle)

• 20 tokens per group per flipped session
• Each group allocates them to other groups, weighing insight · originality · clarity · critical depth
• Cumulative across 6 sessions = 50% of final grade · lecturer evaluation = 50%

Course at a glance (1/3)

Foundations of Digital Disruption in Financial Services

Week 1

22.10.2026

What is ‘emerging tech in finance’, how did we get here, where is it going

• Three waves of digital disruption in finance
• Today’s actors: incumbents, challengers, Big Tech, infrastructure
• Regulatory backdrop: PSD2, MiCA, EU AI Act
• Why now: structural drivers
• What this course will cover

Flipped — Digital Disruption in Financial Services

Week 2

29.10.2026

Group presentations · token allocation · discussion

• Recap of the foundations lecture
• Group presentations on digital disruption
• Token allocation & next steps

Agentic AI & LLMs in Finance

Week 3

05.11.2026

From LLMs to agents · applications · failure modes · EU AI Act

• LLMs in finance: architecture, training, capabilities
• Agentic AI: from answers to actions
• Applications: RAG, robo-advisors, AML, trading agents
• Failure modes: hallucination, drift, prompt injection
• Governance: EU AI Act and high-risk obligations

Flipped — Agentic AI & LLMs in Finance

Week 4

12.11.2026

Group presentations · token allocation · discussion

• Recap of the agentic AI lecture
• Group presentations on real LLM and agent deployments
• Token allocation & next steps

Blockchain, Crypto, DeFi & Tokenisation

Week 5

19.11.2026

From distributed ledgers to MiCA-regulated markets

• Blockchain primer: ledgers, consensus, smart contracts
• Crypto markets: BTC, ETH, stablecoins
• DeFi primitives: AMMs, lending, derivatives
• Tokenisation of real-world assets
• MiCA framework and EU enforcement

Course at a glance (2/3)

Flipped — Blockchain, Crypto, DeFi & Tokenisation

Week 6

26.11.2026

Group presentations · token allocation · discussion

• Recap of the blockchain & DeFi lecture
• Group presentations on real protocols and deployments
• Token allocation & next steps

Fintech Business Models

Week 7

03.12.2026

Neobanks, embedded finance, BNPL, Open Banking, Big Tech in finance

• Neobanks: N26, Revolut, Monzo, Chime
• Embedded finance & BaaS
• BNPL: Klarna, Affirm, regulatory pushback
• Open Banking & PSD2 outcomes
• Big Tech in finance

Flipped — Fintech Business Models

Week 8

10.12.2026

Neobanks, embedded finance, BNPL · group presentations · token allocation

• Recap of the fintech business-models lecture
• Group presentations on real companies and unit economics
• Token allocation & next steps

RegTech, Cybersecurity & Privacy-Preserving Compute

Week 9

17.12.2026

Industrialising compliance · cyber-threat landscape · ZKPs, MPC, federated learning · post-quantum

• RegTech overview: industrialising compliance
• KYC/AML automation in production
• Cybersecurity threats in finance
• Privacy-preserving compute: ZKPs, MPC, federated learning
• Post-quantum cryptography & the migration

Flipped — RegTech, Cybersecurity & Privacy-Preserving Compute

Week 10

07.01.2027

Group presentations · token allocation · discussion

• Recap of the RegTech & security lecture
• Group presentations on vendors, incidents, and emerging tech
• Token allocation & next steps

Course at a glance (3/3)

CBDCs & the Future of Money

Week 11

14.01.2027

Wholesale vs retail design · Digital Euro · e-CNY · programmable money

• What’s a CBDC: wholesale vs retail
• The Digital Euro state of play
• China’s e-CNY and small-country implementations
• Programmable money: feature, threat, or both
• Stablecoins as private money: tension with CBDCs

Flipped — CBDCs & the Future of Money

Week 12

21.01.2027

Final session · group presentations · token allocation · course wrap-up

• Recap of the CBDCs lecture
• Group presentations on real CBDC projects
• Token allocation, final standings, and course retrospective

Assignments / Exams

Flipped-Classroom Presentation Series 100% of your grade

Six in-class group presentations across six emerging-tech topics, graded cumulatively. Each session: 50% peer-allocated tokens + 50% lecturer evaluation.

Group of up to 4.

Submit by emailing oliver.padmaperuma@uni-ulm.de, CC andre.guettler@uni-ulm.de. Subject pattern: Emerging Technology & Finance_assignment-1-flipped-classroom-presentations_surname1_surname2_…

21 January 2027

7.2 Recap from Lecture 6

• 7.1 Course objectives
• 7.2 Recap from Lecture 6
• 7.3 Neobanks
• 7.4 Embedded finance & BaaS
• 7.5 BNPL
• 7.6 Open Banking & PSD2
• 7.7 Big Tech in finance
• 7.8 Conclusion of Lecture 7

• What the flipped session surfaced

What the flipped session surfaced

• DeFi protocols that survived the “TVL is not revenue” critique — usually Uniswap, MakerDAO, Aave — vs the over-leveraged also-rans.
• The most credible RWA tokenisation case the room found — almost certainly tokenised T-bills (BUIDL, OUSG, BENJI).
• A pattern across DeFi hacks: 80%+ are smart-contract logic bugs, not cryptographic failures.

The Week-6 flipped session pivoted from the marketing-grade DeFi narrative (“decentralised finance disrupts banking”) to the operational reality (a thin layer of productive protocols on top of leveraged-circular activity). As you read the recap, notice how the strongest groups separated protocol mechanics from token economics — those two layers are often conflated and confused. Today’s lecture pivots from on-chain financial primitives to off-chain fintech business models. The interesting cross-cutting question is whether neobanks and DeFi are competing for the same future customer or two genuinely separate audiences — a question we’ll revisit in the Week-8 flipped session.

7.3 Neobanks

• 7.1 Course objectives
• 7.2 Recap from Lecture 6
• 7.3 Neobanks
• 7.4 Embedded finance & BaaS
• 7.5 BNPL
• 7.6 Open Banking & PSD2
• 7.7 Big Tech in finance
• 7.8 Conclusion of Lecture 7

• The neobank business model
• Unit economics, simplified

The neobank business model

• Customer acquisition — app-store + paid social + referrals; CAC €15–40 vs incumbents’ €100–300.
• Revenue — interchange (the big one, 0.2–1.4% per card transaction depending on region) · premium subscriptions · FX margins · interest on deposits parked at the central bank · gradually lending.
• Path to profitability — most neobanks took 7–10 years to operating profit; Revolut announced first full-year profit in 2024, N26 announced first quarterly profit in 2024.
• Geographic reality — easier in EU than US (passportable banking licence vs partner-bank dependency).

The neobank story is the canonical time-to-profitability lesson in fintech. The unit economics look attractive on paper (low CAC, scalable infrastructure) but mask two structural issues: revenue per user is structurally lower than incumbents because interchange caps and the lack of branch-based wealth products limit upsell, and the cost of regulatory compliance + customer service scales faster than expected. Revolut hitting profitability in 2024 was a milestone not because the model is unprofitable — interchange is generous enough — but because reaching a critical mass of premium-subscription customers (Revolut Plus, Metal, Ultra) took longer than the original investor case projected (Vives 2017).

Unit economics, simplified

Metric	Neobank	Incumbent bank
Customer acquisition cost (CAC)	€15–40	€100–300 (branch, advisor time)
Revenue per user (annual)	€30–80	€200–400
Cost-to-income ratio	50–70% (improving with scale)	50–60% (stable)
Time to operating profit	7–10 years	n/a (legacy positive)
Premium-subscription share of revenue	20–40% (rising)	<5%

• Neobanks win on CAC — the digital-native acquisition model is structurally cheaper.
• Neobanks lose on revenue per user — narrower product range, customer-deposit balances 1/10 of incumbents’.
• Cross-over depends on premium subscriptions (Revolut Plus/Metal/Ultra) and lending (most neobanks barely lend).
• Lending is the structural bottleneck: neobanks haven’t built the credit-underwriting capability to deploy their deposits profitably.

Use this table as a starting structure but bring current numbers to Week-8 presentations — the published 2024 financials from Revolut, N26, and Monzo all challenge or revise specific cells. The most under-appreciated structural fact is that neobanks hold customer deposits but earn primarily transaction revenue; they don’t earn the net-interest margin that incumbents do because they don’t lend. Until lending capability scales, the revenue ceiling per user is set by interchange + subscription + FX — and that ceiling is structurally lower than incumbents’ net-interest margin. Cite Vives (2017).

7.4 Embedded finance & BaaS

• 7.1 Course objectives
• 7.2 Recap from Lecture 6
• 7.3 Neobanks
• 7.4 Embedded finance & BaaS
• 7.5 BNPL
• 7.6 Open Banking & PSD2
• 7.7 Big Tech in finance
• 7.8 Conclusion of Lecture 7

• What means
• The BaaS stack

What “embedded” means

• Financial primitives (accounts, payments, lending, cards) exposed as APIs to non-finance companies.
• A SaaS firm offers a payment account; a retailer offers BNPL at checkout; a gig platform offers instant pay-out — without becoming a bank.
• The bank that powers the experience is typically invisible to the end user.
• A consumer who uses “Uber Cash” or “Shopify Balance” is interacting with an embedded financial product whose legal counterparty they may not recognise.

Embedded finance is the most under-appreciated structural shift in financial services because consumers don’t see it — by design. Stripe Issuing, Marqeta-powered card products, and Solaris-backed retailer cards are everywhere in 2026, but the brands consumers know (Uber, Shopify, Apple) are not the regulated entities. The interesting governance question is whether consumer-protection regulations have kept up with this layered architecture — when something goes wrong, customers often don’t know which entity to complain to or which regulator supervises it. The 2024 Synapse Financial Technologies collapse in the US is the canonical case study (Frost et al. 2019).

The BaaS stack

• Stripe — payments + treasury + issuing; expanding into lending and capital products.
• Marqeta — card issuing infrastructure; powers Klarna, DoorDash, JPMorgan Chase wallets.
• Solaris, Treezor, Treasury Prime — accounts + cards in Europe.
• Adyen — payments at scale; eBay, Uber, Spotify under the hood.

• Gig platforms (Uber, DoorDash) for instant pay-out.
• SaaS firms (Shopify Pay, Square) for merchant accounts.
• Retailers (Amazon, IKEA) for store cards and BNPL.
• Other fintechs that don’t want their own banking licence.

The BaaS layer creates a structural shift in who distributes financial services: the answer is increasingly anyone with a customer relationship. A logistics SaaS firm doesn’t need to be a bank to offer working-capital lending to its merchants — it can rent the underlying bank via Stripe. The regulatory implication is that the entity holding the licence (the underlying bank, e.g. Cross River, Evolve, or Solaris) carries the regulatory liability while the consumer-facing brand captures most of the value. This arrangement works until something fails; then regulators ask whether the right entity was supervising consumer protection. Cite Frost et al. (2019).

7.5 BNPL

• 7.1 Course objectives
• 7.2 Recap from Lecture 6
• 7.3 Neobanks
• 7.4 Embedded finance & BaaS
• 7.5 BNPL
• 7.6 Open Banking & PSD2
• 7.7 Big Tech in finance
• 7.8 Conclusion of Lecture 7

• What BNPL actually does
• Regulatory recharacterisation

What BNPL actually does

• Splits an online checkout into 4 (or 6, or 12) instalments, typically interest-free for the consumer.
• Provider pays the merchant immediately; collects from consumer over weeks or months.
• Provider revenue: merchant take-rate (4–6% of transaction value — more than card interchange) + late fees (varies by market).
• Provider risk: consumer default, chargebacks, and increasingly regulatory recharacterisation as credit.

The honest economic story of BNPL: it is consumer credit offered at the point of sale, paid for primarily by merchants (who accept a higher take-rate than card interchange in exchange for higher conversion). It works as a business when (a) the merchant uplift exceeds the cost of capital + defaults, (b) the late-fee revenue covers servicing, and (c) regulators don’t reclassify the product into the higher-friction consumer-credit regime. Conditions (a) and (b) held through 2018–22 when interest rates were near zero; condition (c) is failing in 2024–26 (Berg, Fuster, and Puri 2022).

Regulatory recharacterisation

• UK — FCA brought BNPL under credit regulation (in force 2026). Affordability checks required.
• EU — Consumer Credit Directive II (CCD2) covers BNPL; phased application across member states 2025–27.
• US — CFPB rule reclassifying BNPL as credit-card-equivalent (contested in court).
• Australia — BNPL Code of Conduct + new credit-licence regime.

The honest critique of BNPL: it disclosed less than equivalent credit, was usable by under-banked consumers without affordability checks, and contributed to over-indebtedness in some segments. Regulators caught up.

BNPL is the canonical fintech innovation → regulatory-recharacterisation arc. The 2018–22 phase was the innovation phase: rapid scale, generous merchant economics, consumer love. The 2023–26 phase is the recharacterisation phase: regulators conclude this is credit and impose credit-regime obligations (affordability checks, mandatory credit-bureau reporting, robust complaints handling). The interesting strategic response is which providers adjust gracefully (Klarna obtained UK FCA registration ahead of the deadline) vs which double down on minimal compliance and risk market exit. Cite Berg, Fuster, and Puri (2022) for the broader fintech-lending framing.

7.6 Open Banking & PSD2

• 7.1 Course objectives
• 7.2 Recap from Lecture 6
• 7.3 Neobanks
• 7.4 Embedded finance & BaaS
• 7.5 BNPL
• 7.6 Open Banking & PSD2
• 7.7 Big Tech in finance
• 7.8 Conclusion of Lecture 7

• What PSD2 actually mandated
• Did PSD2 deliver competition?

What PSD2 actually mandated

• Banks must expose customer-authorised account data via APIs to licensed third parties — AIS (Account Information Services).
• Banks must allow licensed third parties to initiate payments on behalf of customers — PIS (Payment Initiation Services).
• Strong Customer Authentication (SCA) required for online transactions over €30.
• Goal: catalyse competition by breaking banks’ data monopoly over customer-account information (European Parliament and Council 2015).

PSD2 is the foundational EU fintech regulation — the policy choice in 2015 to force banks to expose customer data to competitors. It is also the most successful test case of regulatory-driven market structure: by 2024, the EU has thousands of licensed AIS/PIS providers, an entire BaaS layer was built on its foundations, and incumbents now openly compete on API-first developer experience because PSD2 made it pointless to be defensive about data. The PSD3 update (proposed 2023, in trilogue 2025) extends the same logic with stronger fraud-liability rules and broader open-finance scope. Cite European Parliament and Council (2015).

Did PSD2 deliver competition?

• TrueLayer, Tink (now Visa), Plaid (US analogue) built thriving aggregator businesses.
• BaaS aggregators monetise PSD2 primitives at scale.
• Mortgage and lending workflows in the UK improved dramatically via Open Banking-powered income verification.
• The B2B Open-Banking infrastructure layer is the clearest commercial win.

• Most consumers don’t directly use PIS for retail payments (cards remain dominant).
• Some bank-controlled APIs degrade competitor experience subtly.
• The biggest consumer-facing improvement (mortgage onboarding) is hidden from end users.
• “Open Banking” the consumer term and PSD2 the legal text are not the same thing.

PSD2 delivered infrastructure-layer competition, not the consumer-facing revolution its supporters originally pitched. That is still a meaningful success — the existence of a competitive aggregator layer is what made later innovations (BaaS, BNPL, embedded lending) possible. But the marketing claim “consumers will switch their main bank to a Plaid-powered fintech” mostly didn’t happen; switching costs remain high and consumer attention is limited. The honest framing is that PSD2 reshaped the upstream of fintech (the API layer) without dramatically reshaping the downstream (consumer banking choice).

7.7 Big Tech in finance

• 7.1 Course objectives
• 7.2 Recap from Lecture 6
• 7.3 Neobanks
• 7.4 Embedded finance & BaaS
• 7.5 BNPL
• 7.6 Open Banking & PSD2
• 7.7 Big Tech in finance
• 7.8 Conclusion of Lecture 7

• The defensive moat thesis
• The land-grab thesis

The defensive moat thesis

• Western Big Tech offers payments not to disrupt banking but to lock users into the ecosystem.
• Apple Pay → keeps users on iPhone (high switching cost).
• Google Pay → ad-targeting data + Android ecosystem stickiness.
• Amazon Lending → seller stickiness on Amazon marketplace.
• The financial product is rarely profitable on its own; it’s a retention tool for the core product.

The Western Big Tech approach to finance is defensive. Apple makes ~$1B/year directly from Apple Card but tens of billions indirectly via iPhone lock-in. Goldman Sachs lost money on the Apple Card partnership and ultimately exited. The lesson is that Big Tech’s financial products are rarely standalone businesses — they are complements to the core platform. Banks that compete against them on a like-for-like basis are competing against a subsidised product. The interesting policy question is whether complement subsidies count as anti-competitive — EU regulators are increasingly leaning yes (Frost et al. 2019).

The land-grab thesis

• Alibaba’s Ant Group — became China’s largest financial-services firm (before 2020–21 regulatory intervention).
• WeChat Pay — primary payment rail for a billion users.
• M-Pesa — primary financial-services rail in Kenya, run by mobile-network operator Safaricom.
• These are offensive plays — finance is the core business, not a feature (Jack and Suri 2014).

The contrast between Western (defensive) and emerging-market (offensive) Big Tech in finance is one of the most consequential structural facts in this course. The reasons for divergence are partly regulatory (China’s PBoC was permissive then assertive; Kenya’s central bank let Safaricom build), partly infrastructural (no incumbent retail banking system in Kenya to defend), and partly political-economic (state tolerance for platform consolidation differs by jurisdiction). For a Bachelor finance audience, the takeaway is that “will Big Tech disrupt banking?” is a regional question — the answer is different in Frankfurt, San Francisco, Hangzhou, and Nairobi. Cite Jack and Suri (2014) for the M-Pesa foundational reference.

7.8 Conclusion of Lecture 7

• 7.1 Course objectives
• 7.2 Recap from Lecture 6
• 7.3 Neobanks
• 7.4 Embedded finance & BaaS
• 7.5 BNPL
• 7.6 Open Banking & PSD2
• 7.7 Big Tech in finance
• 7.8 Conclusion of Lecture 7

• Course at a glance (1/3)
• Course at a glance (2/3)
• Course at a glance (3/3)
• Further reading
• Prepare before next flipped session (Week 8)
• See you next time
• References

Course at a glance (1/3)

Foundations of Digital Disruption in Financial Services

Week 1

22.10.2026

What is ‘emerging tech in finance’, how did we get here, where is it going

• Three waves of digital disruption in finance
• Today’s actors: incumbents, challengers, Big Tech, infrastructure
• Regulatory backdrop: PSD2, MiCA, EU AI Act
• Why now: structural drivers
• What this course will cover

Flipped — Digital Disruption in Financial Services

Week 2

29.10.2026

Group presentations · token allocation · discussion

• Recap of the foundations lecture
• Group presentations on digital disruption
• Token allocation & next steps

Agentic AI & LLMs in Finance

Week 3

05.11.2026

From LLMs to agents · applications · failure modes · EU AI Act

• LLMs in finance: architecture, training, capabilities
• Agentic AI: from answers to actions
• Applications: RAG, robo-advisors, AML, trading agents
• Failure modes: hallucination, drift, prompt injection
• Governance: EU AI Act and high-risk obligations

Flipped — Agentic AI & LLMs in Finance

Week 4

12.11.2026

Group presentations · token allocation · discussion

• Recap of the agentic AI lecture
• Group presentations on real LLM and agent deployments
• Token allocation & next steps

Blockchain, Crypto, DeFi & Tokenisation

Week 5

19.11.2026

From distributed ledgers to MiCA-regulated markets

• Blockchain primer: ledgers, consensus, smart contracts
• Crypto markets: BTC, ETH, stablecoins
• DeFi primitives: AMMs, lending, derivatives
• Tokenisation of real-world assets
• MiCA framework and EU enforcement

Course at a glance (2/3)

Flipped — Blockchain, Crypto, DeFi & Tokenisation

Week 6

26.11.2026

Group presentations · token allocation · discussion

• Recap of the blockchain & DeFi lecture
• Group presentations on real protocols and deployments
• Token allocation & next steps

Fintech Business Models

Week 7

03.12.2026

Neobanks, embedded finance, BNPL, Open Banking, Big Tech in finance

• Neobanks: N26, Revolut, Monzo, Chime
• Embedded finance & BaaS
• BNPL: Klarna, Affirm, regulatory pushback
• Open Banking & PSD2 outcomes
• Big Tech in finance

Flipped — Fintech Business Models

Week 8

10.12.2026

Neobanks, embedded finance, BNPL · group presentations · token allocation

• Recap of the fintech business-models lecture
• Group presentations on real companies and unit economics
• Token allocation & next steps

RegTech, Cybersecurity & Privacy-Preserving Compute

Week 9

17.12.2026

Industrialising compliance · cyber-threat landscape · ZKPs, MPC, federated learning · post-quantum

• RegTech overview: industrialising compliance
• KYC/AML automation in production
• Cybersecurity threats in finance
• Privacy-preserving compute: ZKPs, MPC, federated learning
• Post-quantum cryptography & the migration

Flipped — RegTech, Cybersecurity & Privacy-Preserving Compute

Week 10

07.01.2027

Group presentations · token allocation · discussion

• Recap of the RegTech & security lecture
• Group presentations on vendors, incidents, and emerging tech
• Token allocation & next steps

Course at a glance (3/3)

CBDCs & the Future of Money

Week 11

14.01.2027

Wholesale vs retail design · Digital Euro · e-CNY · programmable money

• What’s a CBDC: wholesale vs retail
• The Digital Euro state of play
• China’s e-CNY and small-country implementations
• Programmable money: feature, threat, or both
• Stablecoins as private money: tension with CBDCs

Flipped — CBDCs & the Future of Money

Week 12

21.01.2027

Final session · group presentations · token allocation · course wrap-up

• Recap of the CBDCs lecture
• Group presentations on real CBDC projects
• Token allocation, final standings, and course retrospective

Further reading

• Vives (2017) — concise 12-page framing on FinTech vs banking.
• Frost et al. (2019) — BIS WP on Big Tech in financial intermediation.
• Berg, Fuster, and Puri (2022) — Annual Review chapter on FinTech lending.
• European Parliament and Council (2015) — PSD2 text; focus on AIS / PIS / SCA definitions.

Prepare before next flipped session (Week 8)

1. Pick your Week-8 angle from the presentation series brief.
2. Bring real unit-economics — CAC, LTV, take-rate, default rate, net-interest margin. Numbers, not vendor narrative.
3. Pick a counterargument to your thesis — peers will probe it in Q&A. Anticipating one beats answering one cold.
4. Upload slide PDF to Moodle before 14:00 next Thursday.

See you next time

Reminder

• Next session: Lecture 8 — Flipped: Fintech Business Models on 10 December 2026.
• Each group presents (6 min + 2 min Q&A).
• Slides due on Moodle before 14:00.

References

Berg, Tobias, Andreas Fuster, and Manju Puri. 2022. “FinTech Lending.” Annual Review of Financial Economics 14: 187–207. https://doi.org/10.1146/annurev-financial-101521-112042.

European Parliament and Council. 2015. “Directive (EU) 2015/2366 on Payment Services in the Internal Market (PSD2).” Official Journal of the European Union, L 337/35. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32015L2366.

Frost, Jon, Leonardo Gambacorta, Yi Huang, Hyun Song Shin, and Pablo Zbinden. 2019. “BigTech and the Changing Structure of Financial Intermediation.” BIS Working Paper 779. Bank for International Settlements. https://www.bis.org/publ/work779.htm.

Jack, William, and Tavneet Suri. 2014. “Risk Sharing and Transactions Costs: Evidence from Kenya’s Mobile Money Revolution.” American Economic Review 104 (1): 183–223. https://doi.org/10.1257/aer.104.1.183.

Vives, Xavier. 2017. “The Impact of FinTech on Banking.” European Economy: Banks, Regulation, and the Real Sector, no. 2: 97–105.