Asset & Wealth Management: how the sector works
how asset and wealth management works: the AUM-and-fees model, active vs passive, the value chain from manager to distributor to client, and fiduciary duty.
This block builds foundational fluency in Asset & Wealth Management, covering how capital flows from investors through managers, distributors, and custodians to markets. You will understand the value chain across active and passive strategies, retail and institutional channels, and the economics behind AUM-based fees. It maps the competitive landscape of incumbents like BlackRock and Vanguard against challengers, private markets specialists, and fintech distributors, and explains where margin concentrates. You will learn the regulatory architecture governing fiduciary duty, disclosure, and investor protection in the US and Europe, plus the core metrics, acronyms, and calculations professionals use daily to size markets, price fees, and assess fund performance and flows.
What you'll master
- Map the asset and wealth management value chain from asset owners through managers, distributors, and custodians, identifying where fees and margin accrue
- Distinguish major player types and explain competitive dynamics between passive giants, active managers, private markets firms, and wealth platforms
- Identify the key regulations and bodies (SEC, MiFID II, UCITS, fiduciary rules) and the compliance constraints they impose in practice
- Calculate core metrics such as AUM, fee yield, expense ratios, and net flows, and run basic due diligence on a fund or manager
Key terms
Modules
Covers the AUM-and-fees business model, active versus passive economics, the distribution chain, and fiduciary conflicts.
Maps the incumbents, challengers, gatekeepers and suppliers, and where margin actually lands across the sector.
Explains the regulators, major laws, AML and KYC obligations, and how to build a compliant asset management function.
Covers the essential numbers, acronyms, weekly calculations and due-diligence checks used in the industry.
Latest articles
Recent articles from the blog that apply to Asset & Wealth Management.
- 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.
- MarketingThree meetings with a consultant won't move the needle: how fund selectors actually change their approved listsWinning a slot on a platform approved list or consultant buy list is the distribution chokepoint that determines whether an asset manager grows or stagnates. This playbook sets out the specific steps, common failures, and quick wins for marketing leaders who need to move gatekeepers from aware to allocated.
- FinanceFee compression and the flight to passive: why the death of active management is overstatedNet flows into passive vehicles have dominated industry headlines for a decade, and the consensus now reads active management as a structurally declining business. The reality is more complicated, and the CFOs who misread it will make poor capital allocation decisions at exactly the wrong moment.
- DataHow JPMorgan Chase built data contracts across 50+ domainsJPMorgan Chase spent years grappling with fragmented data ownership across hundreds of business lines before systematically formalizing who owns what and on what terms. Their approach to data contracts offers a working model for CDOs who need accountability without organizational paralysis.
- AIHow Goldman Sachs built an AI usage policy that employees actually followedMost corporate AI policies sit in a shared drive and change nothing. Goldman Sachs took a different path, and the mechanics of how they did it offer a transferable model for any team serious about governing AI in practice.
- AIRight context, wrong assumption: what Morgan Stanley learned about prompting at scaleMorgan Stanley's deployment of an AI assistant for its financial advisors exposed a problem most teams overlook: feeding the model more information does not produce better answers. The real discipline is selecting which context matters, and why that distinction changes how you build prompts entirely.