Banking
How banks actually work, and the finance, marketing, data and AI that run them.
The programs
Five programs, one per domain of the Banking sector. Every one is readable straight away, without an account, and you can gauge your level on any of them whenever you want.
Banking
This block builds the foundational fluency every banking professional needs before specializing.
18 lessons · about 4h
- Explain the end-to-end banking value chain from deposit-taking and underwriting to distribution and risk management
- Identify major players, their competitive positioning, and how margin is distributed between incumbents, challengers, and intermediaries
- Interpret core banking regulations and explain what compliance obligations they impose on daily operations
Finance for Banking
This block builds sector-specific financial fluency for banking professionals.
31 lessons · about 6h
- Explain the end-to-end banking value chain from deposit-taking and underwriting to distribution and risk management
- Identify major players, their competitive positioning, and how margin is distributed between incumbents, challengers, and intermediaries
- Interpret core banking regulations and explain what compliance obligations they impose on daily operations
Marketing for Banking
Banking marketing sits at the intersection of trust, regulation and long product-consideration cycles.
31 lessons · about 6h
- Explain the end-to-end banking value chain from deposit-taking and underwriting to distribution and risk management
- Identify major players, their competitive positioning, and how margin is distributed between incumbents, challengers, and intermediaries
- Interpret core banking regulations and explain what compliance obligations they impose on daily operations
Data for Banking
Banking runs on data: payment flows, credit bureau feeds, transaction logs, core banking systems, and regulatory reporting all generate structured and unstructured datasets that drive lending, risk, and compliance decisions.
31 lessons · about 6h
- Explain the end-to-end banking value chain from deposit-taking and underwriting to distribution and risk management
- Identify major players, their competitive positioning, and how margin is distributed between incumbents, challengers, and intermediaries
- Interpret core banking regulations and explain what compliance obligations they impose on daily operations
AI for Banking
AI is reshaping banking across credit decisioning, fraud detection, trading, customer service, and compliance, but separating genuine capability from vendor hype requires structured judgment.
31 lessons · about 6h
- Explain the end-to-end banking value chain from deposit-taking and underwriting to distribution and risk management
- Identify major players, their competitive positioning, and how margin is distributed between incumbents, challengers, and intermediaries
- Interpret core banking regulations and explain what compliance obligations they impose on daily operations
Banking runs on trust, regulation, and the management of risk and liquidity. From how a bank makes money on the spread to Basel capital rules, deposit dynamics, and the fintech pressure reshaping it, this vertical gives you the sector's big picture, then finance, marketing, data and AI applied inside it.
Key terms
Why specialize in Banking?
Cross-cutting skills are not enough in the Banking 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 Banking, with the calculations, benchmarks and checklists you actually need on the ground.
What you'll be able to do
- Understand how the Banking 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 Banking (US and Europe markets)
- Apply finance, marketing, data and AI to the realities of Banking
- 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
Banking: how the sector works
Generalhow a bank works and makes money: deposits, lending, net interest margin, the balance sheet, regulation (Basel, deposit insurance), and the retail vs corporate vs investment banking split.
4 Modules · 18 Lessons
Finance in banking
Financebank-specific finance: capital adequacy and Basel ratios, liquidity and funding, credit risk provisioning, and why a bank's own P&L and balance sheet read unlike any other company.
3 Modules · 13 Lessons
Marketing in banking
Marketingmarketing regulated financial products: trust and brand, acquisition economics, cross-sell and primary-bank relationships, fair-treatment and disclosure rules that constrain messaging.
3 Modules · 13 Lessons
Data in banking
Datathe data that runs a bank: transactions, credit scoring, fraud and AML detection, risk models, and the strict governance, privacy and model-risk rules around them.
3 Modules · 13 Lessons
AI in banking
AIAI in banking: fraud detection, credit decisioning, customer service, and the heavy constraints of explainability, fairness and regulatory model governance.
3 Modules · 13 Lessons
Prefer to place yourself first?
Five short assessments, one per domain of the Banking 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 Banking, across every discipline.
- FinanceKKR flags AI concentration risk: what the credit binge means for bank NPL ratios nowKKR's warning about overexposure to AI-related borrowing is not just a private credit concern. For bank CFOs managing credit portfolios, it reopens a familiar and uncomfortable set of questions about NPL ratios, coverage adequacy, and whether today's cost of risk accurately prices tomorrow's defaults.
- 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.
Frequently asked questions
What does the Banking vertical cover?
It covers how banks make money and how they are run, in five blocks: how the sector works (deposits, lending, net interest margin, regulation, retail vs corporate vs investment banking), then finance, marketing, data and AI applied inside a bank. The idea is to give you the sector's big picture first, then each function seen through banking's own constraints.
Who is it for if I don't work in a bank?
It fits anyone who sells to banks, advises them, invests in them or works in fintech and needs to understand the buyer's constraints. Capital ratios, liquidity and model governance explain most of what a bank will and will not do, including why decisions take longer than elsewhere.
Do I need a finance background to start?
No. The first block explains how a bank works from scratch: where deposits come from, how lending generates income, what sits on the balance sheet, and what regulation demands. The finance block goes deeper afterwards, once the mechanics are clear.
Where should I start if I only have limited time?
Start with the sector block on how banking works, then go straight to the block matching your role. The finance, marketing, data and AI blocks are written to be read independently, but they all assume you know how a bank earns its margin.
Why does a bank's P&L read differently from other companies?
Because a bank's raw material is money: its revenue comes largely from the spread between what it pays on deposits and earns on loans, and its balance sheet is the business rather than a support to it. Add capital adequacy requirements and credit risk provisioning, and standard ratio analysis breaks down. The finance block covers exactly these differences.
What is different about marketing a regulated financial product?
Messaging is constrained: fair-treatment and disclosure rules limit what you can claim, so trust and brand carry more weight than clever copy. The marketing block also covers acquisition economics, cross-sell, and why becoming the customer's primary bank changes the value of the relationship.
How much of the data and AI content deals with regulation rather than technique?
A substantial part, because in banking the constraint is the subject. Credit scoring, fraud and AML detection and risk models all sit under model-risk governance, privacy rules and explainability and fairness requirements, which shape what can actually be deployed.
Which terms should I be comfortable with after reading?
Net interest margin, Basel III and capital requirements, liquidity, KYC / AML, credit risk and open banking. These six recur across the five blocks and are the vocabulary you need to follow a conversation with a bank's finance, risk or compliance teams.