The six disruptions (and what each one breaks)
1) Performance becomes the product (and beta economics keep eroding)
When clients can compare outcomes instantly, alpha (net performance) becomes the brand, and “product shelves” become utilities. In passive investing, margins are expected to shrink by ~15 bps by 2030 (about 6% CAGR compression), pushing banks to either (a) build differentiated performance engines or (b) stop competing in commoditized beta.
Strategic implication: distribution without differentiated outcomes becomes a low-multiple business.
What to measure: net flows by segment, performance persistence vs benchmarks, “all-in” client net returns (fees + taxes + execution), share of wallet in advice-led mandates.
2) Advice shifts from relationship-centric to algorithmic at scale
Full-stack digital and AI make it possible to deliver advice and service at radically lower unit cost – especially attractive to younger and mass-affluent segments. But as switching costs fall, trust and credibility must be designed into the model, not assumed from legacy brand.
Strategic implication: banks need an advice proposition that is simultaneously scalable and trusted – with clear accountability for recommendations and outcomes.
What to measure: cost-to-serve per advised household, digital engagement, conversion from self-directed to advised, retention after market drawdowns.
3) Asset managers move direct-to-consumer (D2C) and capture the interface
Manufacturers are moving closer to end clients, taking the relationship, the data, and the economics. One large provider’s advice unit was carved out with ~$900B client assets (2024) – a scale signal that D2C advice is no longer niche.
Strategic implication: if banks lose the interface, they lose: (1) data advantage, (2) cross-sell optionality, and (3) long-run pricing power.
What to measure: share of new-to-bank clients sourced via “feeder” channels, referral economics, lead-to-funded conversion, client data completeness.
4) Banking becomes plug-and-play (APIs + real-time rails shift value to access owners)
Open finance and real-time rails make the stack modular: specialists can “snap in” to deliver parts of the journey, siphoning economics from incumbents. Winning tends to go to whoever controls access (the front door) and orchestrates the ecosystem.
Strategic implication: banks must decide whether they will be (a) ecosystem gatekeepers, (b) embedded specialists, or (c) commodity infrastructure.
What to measure: platform/API adoption, partner-driven revenue, ecosystem retention, and time-to-launch for new propositions.
5) New asset forms reshape the “capital stack” (stablecoins, tokenized assets, private credit)
Three forces are colliding:
- Stablecoins and tokenized assets broaden access and reduce settlement friction.
- Real-time rails + blockchain shift value away from legacy intermediaries.
- Private credit expands off balance sheet while attracting borrower demand and investor capital.
Stablecoins’ scale is no longer theoretical: market capitalization surpassed $300B in October 2025.
Regulators are also accelerating frameworks that shape who can operate, how reserves are managed, and what “safe” usage looks like – e.g., UK authorities have publicly prioritized stablecoin payments in their 2026 agenda.
Strategic implication: banks must clarify their role in the new stack – integrator, originator, infrastructure provider, or some hybrid – because deposit and payments economics are directly exposed.
What to measure: share of transactional flows at risk, economics of custody/settlement, tokenization pipeline, private-credit distribution economics, balance sheet vs fee mix.
6) “Borders are back” (local regulation and geopolitics matter more)
Rising trade restrictions and political risk are slowing globalization and fragmenting regulatory regimes. The claim of a fivefold rise in trade restrictions (2015–2023) signals the direction of travel.
Independent tracking reinforces the pattern: in the first 10 months of 2025, more than 2,500 trade restrictions were imposed globally – almost five times the level in the same period of 2015.
Strategic implication: in some corridors, local agility and regulatory fluency create more advantage than global breadth.
What to measure: cross-border revenue sensitivity, jurisdiction-by-jurisdiction compliance cost, product portability, and local partner leverage.
Three viable strategic archetypes
When the ground is moving, “do everything” is the highest-risk strategy. A sharper approach is to choose an archetype and build around it.
A) The Digital Gatekeeper
Own the front door: a scaled mobile experience plus an API ecosystem connecting banking, SMB, and wealth – powered by rich data and partner contributions.
Best when: you have strong retail/SMB distribution, high engagement, and the ability to run ecosystem economics.
B) The Alpha Powerhouse
Win on outcomes: a large-scale balance sheet + differentiated investment engines (including AI-driven insight) and selective growth hubs.
Best when: you can credibly generate repeatable performance and package it into trusted advice.
C) The Asset-Light Orchestrator
Minimize balance sheet intensity and coordinate best-of-breed partners across manufacturing and advice – keeping ownership of the relationship while outsourcing components.
Best when: you have relationship strength but need speed and flexibility more than vertical integration.
A practical 10-week “new playbook” sprint
- Value-chain heatmap (Weeks 1–2): where profits are made today vs where value is migrating (interface, data, outcomes, rails).
- Archetype choice (Weeks 2–3): pick one primary path; define what you will stop doing.
- Build–buy–partner roadmap (Weeks 3–6): identify missing capabilities (AI advice, ecosystem APIs, alt distribution, tokenization/custody, private credit access).
- Interface ownership plan (Weeks 5–8): permissioned data strategy + advice experience design + feeder-channel growth plan.
- Investor-grade metrics (Weeks 8–10): a small KPI set that proves progress (flows, unit costs, engagement, fee mix, platform economics).
To pressure-test decisions, leadership teams should ask “high-gain” questions such as whether to double down on differentiated alpha or exit commoditized beta, how to convert proprietary data into a mass-market advisory moat, and which build–buy–partner moves protect ownership of the client interface in an open-finance ecosystem.
