New Era for Finance | World Economic Forum Annual Meeting 2026

High-level takeaways

Technology — especially AI, blockchain/crypto, tokenization and payment rails — is rewiring finance. This creates large opportunities but also new systemic and operational risks. Trust, regulation and risk management will determine whether innovations scale safely.

• AI is repeatedly flagged as the single biggest disruptive force across customer experience, operations, product distribution and risk management. It also introduces new failure modes (e.g., synchronized trading, AI-enabled fraud, limits to “accuracy”).
• Blockchain/crypto shows commercial strengths (exchanges, stablecoins) and promising uses (tokenization, back‑end payment infrastructure). Consumer crypto payments and many speculative meme/NFT use cases remain uncertain.

Assets, instruments, sectors and entities mentioned

• Crypto: Bitcoin, stablecoins, tokenized assets, NFTs, meme coins (Dogecoin referenced indirectly)
• Exchanges and market comparators: Binance; Shanghai Stock Exchange; New York Stock Exchange
• Traditional markets/instruments: US government securities, collateral management, deposits, pension funds
• Market infrastructure/settlement: T+1 settlement, real-time payments, central clearing
• Companies/events referenced: ING Group, BNY (Bank of New York), Primavera Capital (Fred Hu), Binance, SVB (Silicon Valley Bank), FTX, Terra/Luna (LUNA USD)
• Misc: remittances, cross-border payments, eKYC

Key numbers called out

• ING: ~41 million retail customers; ~85% prefer/use mobile; ~85% of retail sales via mobile.
• Binance: ~300 million users; claimed trading volume higher than Shanghai and NYSE in the prior year; withdrawal stress example — ~ $7bn in one day, ~ $14bn in that week (Dec 2023).
• SVB run: historically, large bank runs removed ~10–15% of deposits over ~2–3 weeks; SVB lost ~80–90% of deposits within 1–2 days.
• Europe: GDP ≈ $16 trillion; ≈ $12 trillion sitting in bank deposits (argument to shift more savings into investment).

Methodologies, frameworks and practical steps

ING — three-layered AI deployment framework

1. Internal tooling and product development (use AI to speed coding and product build).
2. Customer-process automation (lending workflows, contact centers).
3. Customer-facing product distribution/personalization (agentic AI to improve how products are offered).

BNY — risk-infrastructure playbook for faster markets

• Real-time risk detection and proactive risk management.
• Shorter settlement cycles (T+1), real-time payments and central clearing, especially for government securities.
• Improved collateral location, valuation and movement capability.

Risk-management and operational hardening (practical measures)

• Real-time monitoring and early-warning systems (including AI-based detection).
• Network compartmentalization, cloud differentiation and segmentation to limit contagion.
• Activity-based regulation: same activity → same regulatory treatment across entity types.
• Regulatory passporting for near-term cross-border coordination (recognize a single license across jurisdictions).

Investment/capital mobilization (European context)

• Policy and incentives to shift deposits into productive investments to support pensions and future growth.

Risks, cautions and failure modes

• Speed-related risks:
  • Faster withdrawals and online communication accelerate bank runs (SVB example). Speed exposes problems sooner.
  • Algorithmic/AI-driven synchronized trading could amplify volatility and cause systemic shocks.
• AI shortcomings:
  • AI often produces “good but imperfect” outputs — acceptable for 80% solutions but risky where precision and liability matter.
  • Potential for AI-enabled fraud and market manipulation.
• Crypto-specific cautions:
  • Low consumer uptake of crypto payments (El Salvador cited as insufficient adoption).
  • Meme/NFT use cases are highly speculative; many may fade though some cultural tokens persist.
  • Tokenization and crypto-backed rails have promise but regulatory clarity is uneven.
• Interconnectedness/contagion:
  • Digitalization increases the speed and reach of contagion; containment requires better real-time tools and structural design.
• Regulatory fragmentation:
  • Jurisdictions differ in priorities (e.g., China: tighter conduct/product approvals; US: innovation-oriented; EU: AI Act with risk focus).
  • Lack of harmonized global crypto/AI rules increases compliance complexity and regulatory arbitrage.

Policy and regulatory recommendations

• Favor activity-based regulation (same activities get same rules regardless of entity type).
• Use AI and real-time surveillance as part of regulatory toolkits to detect emerging systemic risk.
• Consider pragmatic cross-border approaches first (passporting/recognition of licenses) rather than creating a brand-new global regulator.
• Balance regulation to be safety-focused yet pro-innovation — design rules that support adoption and trust.
• Encourage shortening settlement times and strengthening market plumbing (T+1, central clearing, real-time payments) to reduce operational and liquidity risk.

Investment and portfolio implications

• Institutional priorities:
  • Real-time risk detection, collateral visibility, and settlement efficiency.
• Potential investable areas:
  • Tokenization platforms, enterprise blockchain infrastructure, payment bridges between fiat and crypto rails, AI tools for risk management and customer automation.
• Cautions for investors:
  • Speculative crypto segments (memes/NFTs) and unproven consumer crypto payments are high-risk. Evaluate trust, regulatory clarity and real utility.
• For retail savers (European context):
  • Policy efforts and financial education likely needed to move savings from low-yield deposits into higher-return investments to address pension/growth gaps.

Notable initiatives and examples

• Banks experimenting with blockchain payment systems and stablecoin projects (a European “stablecoin initiative” referenced).
• BNY promoting market-structure upgrades (T+1, central clearing, real-time collateral management).
• Binance (CZ) engaging with multiple governments on tokenization and regulation; several countries consulting on crypto rules (UAE, Bahrain, others).

Explicit recommendations and cautions for practitioners

• Put trust and client-centricity at the core of innovation; innovations that erode trust will fail to scale.
• Deploy AI thoughtfully: use it for monitoring and risk mitigation as well as customer service and productization.
• Prepare for faster failure modes: enhance liquidity management and run faster stress tests.
• Favor responsible deployment and proactive regulatory engagement.
• Consider portfolio exposure to crypto carefully: exchanges and stablecoins are more established; other crypto use cases require stricter vetting.

Disclosures and notable statements

• The transcript contained no explicit “not financial advice” statements.
• CZ noted he had “retired/ex” and thus might speak with fewer constraints.
• Panelists described ongoing investments and advisory conversations with governments (e.g., CZ advising governments; banks participating in stablecoin initiatives).

Presenters / sources

• Steven Van Riik — CEO, ING Group (Netherlands) [transcript spelling]
• J. Coffrey — Chief Enablement & Global Affairs Officer, BNY (Bank of New York) [transcript spelling]
• Fred Hu — Founder, Chairman & CEO, Primavera Capital Limited (China)
• Changpang Jiao (“CZ”) — CEO, Binance (UAE) — noted as retired/ex in transcript
• Session held at: World Economic Forum Annual Meeting 2026 (Davos)

Note: The transcript used for this summary contained auto-generation errors; some names and program/initiative spellings are reproduced as they appeared in the subtitles.