Fintech
How fintech works, and the finance, marketing, data and AI that run it.
The programs
Five programs, one per domain of the Fintech sector. Every one is readable straight away, without an account, and you can gauge your level on any of them whenever you want.
Fintech
Fintech reshapes how money moves, is stored, lent, and invested by unbundling traditional banking into specialized digital services.
18 lessons · about 4h
- Map a fintech value chain end to end and identify where margin and value actually concentrate
- Assess competitive position of an incumbent, challenger, or platform player given power dynamics in the chain
- Identify which regulations and licenses apply to a given fintech business model and what compliance they require
Finance for Fintech
This block builds sector-specific financial fluency for fintech.
31 lessons · about 6h
- Map a fintech value chain end to end and identify where margin and value actually concentrate
- Assess competitive position of an incumbent, challenger, or platform player given power dynamics in the chain
- Identify which regulations and licenses apply to a given fintech business model and what compliance they require
Marketing for Fintech
Fintech marketing operates under tighter constraints than most consumer sectors: regulators scrutinize claims, acquisition costs are inflated by trust deficits and compliance friction, and retention hinges on activation events like funding an account or completing KYC.
31 lessons · about 6h
- Map a fintech value chain end to end and identify where margin and value actually concentrate
- Assess competitive position of an incumbent, challenger, or platform player given power dynamics in the chain
- Identify which regulations and licenses apply to a given fintech business model and what compliance they require
Data for Fintech
This block builds sector-specific data fluency for fintech professionals.
31 lessons · about 6h
- Map a fintech value chain end to end and identify where margin and value actually concentrate
- Assess competitive position of an incumbent, challenger, or platform player given power dynamics in the chain
- Identify which regulations and licenses apply to a given fintech business model and what compliance they require
AI for Fintech
AI is reshaping fintech across credit decisioning, fraud detection, trading, underwriting, and customer engagement, but hype often outpaces deployable value.
31 lessons · about 6h
- Map a fintech value chain end to end and identify where margin and value actually concentrate
- Assess competitive position of an incumbent, challenger, or platform player given power dynamics in the chain
- Identify which regulations and licenses apply to a given fintech business model and what compliance they require
Fintech rebuilds financial services with software, unbundling banks and embedding finance into other products. From payments to lending to embedded finance, this vertical gives you the big picture, then finance, marketing, data and AI applied inside it.
Key terms
Why specialize in Fintech?
Cross-cutting skills are not enough in the Fintech sector. It plays by its own rules: a distinct value chain, specific players and balance of power, dense regulation, and key figures you won't find anywhere else. This vertical gives you that sector fluency: first the big picture, then finance, marketing, data and AI applied concretely to Fintech, with the calculations, benchmarks and checklists you actually need on the ground.
What you'll be able to do
- Understand how the Fintech sector works: value chain, key players and balance of power
- Know the major regulations and laws, the sector's acronyms and vocabulary
- Run the key calculations and read the benchmarks specific to Fintech (US and Europe markets)
- Apply finance, marketing, data and AI to the realities of Fintech
- Gauge your level with 5 sector assessments and a competency radar
70 lessons across 16 modules and 5 blocks, with a test per lens and a sector competency radar. Free to read, no account required.
The curriculum in detail
Fintech: how the sector works
Generalhow fintech works: unbundling and re-bundling financial services, the main models (payments, lending, neobanks, embedded finance), and the regulatory/licensing reality.
4 Modules · 18 Lessons
Finance in fintech
Financefintech unit economics: take rates and interchange, the cost of funds and risk in lending, path to profitability, and how fintechs are valued.
3 Modules · 13 Lessons
Marketing in fintech
Marketingfintech growth: acquisition and trust for money products, virality and referrals, embedded distribution, and compliance in messaging.
3 Modules · 13 Lessons
Data in fintech
Datafintech data: transaction and behavioral data, alternative-data underwriting, fraud and KYC/AML, and data governance.
3 Modules · 13 Lessons
AI in fintech
AIAI in fintech: underwriting and fraud, personalization, support automation, and the fairness/regulatory constraints.
3 Modules · 13 Lessons
Prefer to place yourself first?
Five short assessments, one per domain of the Fintech sector. Ten questions each, three minutes, and a competency radar once you have taken more than one.
All sector assessmentsLatest articles
What the blog publishes on Fintech, across every discipline.
- AISR 11-7 still bites, and gradient boosting just made the wound worseSR 11-7 was written for logistic regression, but banks are now deploying gradient boosting, neural networks, and foundation model-powered scoring into production. This piece unpacks what explainability actually means under the guidance, why examiners are pushing harder on it in 2026, and where the governance frameworks genuinely break down.
- Finance$800mn at an $8bn floor: what Airtel Money's London IPO demands from an African fintech CFOAirtel Money is preparing to file prospectus documents for what could be one of London's largest listings in recent years, targeting $800mn in proceeds at a valuation of $8bn to $9bn. The preparation required to reach that point tells CFOs more about IPO readiness than any generic checklist.
- DataThree pipeline design decisions that determine whether your AML model survives its first regulatory examinationMost fintech fraud and KYC/AML pipelines fail not because the models are weak but because the data architecture cannot defend itself under examination. This playbook walks through the design sequence that keeps you compliant, explainable, and operationally credible when regulators arrive.
- FinanceRevolut's dual listing play and what it signals for fintech CFOsRevolut is preparing to list simultaneously in New York and London, a structural choice that reveals as much about equity story architecture as it does about exchange selection. For CFOs in high-growth fintech, the decisions behind that choice are worth studying carefully.
- AIBuilding credit decisioning models that survive fair-lending scrutinyAI-driven credit models can cut decisioning time and expand credit access, but a single fair-lending violation can trigger enforcement actions that dwarf any efficiency gain. This playbook shows banking AI leaders how to build, document, and defend models that hold up when the OCC, CFPB, or DOJ come knocking.
- DataHow Tala built a credit engine for the world's most invisible borrowersTala lends to borrowers who don't exist in any credit bureau, using smartphone data as a substitute for a credit file. Here is what their model actually does, what it has produced, and what fintech data leaders can reasonably take from it.
Frequently asked questions
What does the Fintech vertical cover?
It covers how the fintech sector works, then finance, marketing, data and AI applied inside it. Five blocks in total: one general block on the sector's models and regulation, plus one block per discipline. The starting point is that fintech rebuilds financial services with software, unbundling banks and embedding finance into other products.
Who is this for if I don't work at a fintech?
It works for anyone whose product touches payments or credit: retailers adding checkout financing, SaaS companies embedding payouts, investors screening the sector. The embedded finance and licensing/BaaS material is written for people who buy financial infrastructure rather than build it.
Where should I start?
Start with the general block, "Fintech: how the sector works", which lays out unbundling and re-bundling, the main models (payments, lending, neobanks, embedded finance) and the regulatory reality. The finance, marketing, data and AI blocks assume you already know which model you're operating in, since take rates and underwriting behave very differently in payments than in lending.
What's the difference between a fintech and a licensed bank?
A licensed bank holds the regulatory permission to take deposits and lend on its own balance sheet; most fintechs don't, and rent that permission through a partner bank or a BaaS provider. This choice drives your economics and your compliance load, which is why licensing sits in the general block alongside the sector's business models.
How do fintechs actually make money?
Mostly through take rates and interchange on payment flows, or through the spread between the cost of funds and the price of credit in lending. The finance block covers both, plus the cost of risk, the path to profitability and how fintechs get valued.
What makes fintech marketing different from marketing any other product?
Trust and compliance. People hand over money and identity documents, so acquisition depends on credibility as much as on channel performance, and every claim about rates, returns or protection is regulated. The marketing block covers acquisition and trust for money products, virality and referrals, embedded distribution, and compliance in messaging.
Does the data block deal with fraud and KYC/AML, or only analytics?
Both. The data block in fintech covers transaction and behavioral data, alternative-data underwriting, fraud detection with KYC/AML, and data governance. Fraud and identity checks are treated as data problems here, not just as compliance checkboxes.
Does the AI block address the regulatory limits on automated credit decisions?
Yes. Alongside underwriting, fraud, personalization and support automation, the AI block covers the fairness and regulatory constraints that apply when a model influences who gets credit and at what price. Explainability and discrimination risk are part of the subject, not an afterthought.