3 Financial Stocks Making Big AI Moves in 2026
Big tech doesn't have a monopoly on AI. Financial firms are moving fast, threading AI into products, workflows, and balance-sheet strategies. If you work in finance, these shifts affect execution, costs, and competitive positioning in 2026.
Robinhood: Cortex could reignite engagement and order flow
Amid buzz around its prediction markets expansion, Robinhood quietly teed up Cortex, an AI assistant for Gold subscribers. The pitch: help retail investors generate ideas using generative AI. If adoption sticks, it can lift engagement and trading volumes at a time when activity has cooled.
- What to watch: Gold attach rate, weekly active users, average trades per user, and volume by product (equities, options, crypto).
- Risk flags: hallucinated outputs, suitability issues, disclosures, and supervisory review of AI-assisted ideas.
Operator takeaway: build tight guardrails and clear UX disclosures, route AI outputs through explainability checks, and track whether Cortex queries correlate with higher-quality trades or just more churn.
JPMorgan Chase: Agentic AI to compress cycle times and unit costs
JPMorgan is deploying agentic AI to speed internal work, including investment banking pitch materials. The near-term payoff is productivity and cost per deliverable; the longer-term edge is service quality and speed that competitors struggle to match.
- KPIs: cycle time reduction per task, cost per document, approval rework rates, and control findings tied to AI use.
- Execution must-haves: human-in-the-loop, model governance, retrieval security for sensitive data, and rigorous audit trails.
Strategic angle: early operational wins can justify continued AI spend, support pricing power, and help protect the valuation premium if the bank shows measurable savings without compliance slippage.
PayPal: PYUSD steps into AI financing rails
After a tough 2025 for the stock, PayPal's stablecoin, PYUSD, just picked up a new use case: USD.AI plans to denominate loans to AI companies in PYUSD. That puts PYUSD closer to real economic activity, not just transfers.
- What matters: PYUSD velocity, partner count, spread economics, on/off-ramp friction, and clarity on stablecoin rules.
- Risk lens: counterparty risk at lenders, liquidity during stress, and competitive pressure from other stablecoins.
If PYUSD volume scales with credible partners, it can add fee and float opportunities. Pair that with cost discipline and easing growth concerns, and a rerate off low forward multiples is possible.
Action checklist for finance teams
- Set measurable AI KPIs: cycle time, unit cost, quality, risk metrics. Report them like financials.
- Adopt human-in-the-loop by default; automate handoffs, not judgment.
- Build a product-level AI policy: data use, disclosures, auditability, rollback plans.
- Prioritize use cases tied to revenue or capital efficiency, not demos.
Want a quick scan of practical tools? See this curated list of AI tools for finance: AI tools for finance.
Bottom line: Robinhood is leaning into AI to spark activity, JPMorgan is pushing for structural efficiency, and PayPal is turning PYUSD into plumbing for AI-linked finance. Track adoption, economics, and risk controls-not press releases-to separate signal from noise in 2026.
Your membership also unlocks:
