← back to rankingFIS · Fidelity National Information Services, Inc.
Information Technology Services · mkt cap $22.0B · calls: Q1 FY2026 vs Q4 FY2025
63.0 conviction · conf-adj 61
conf 4/10 partial
enthusiasm:27.0 · trend:8 · quantifies:5 · impact:0 · under_radar:14 · credibility:-5 · business_impact:8 · disruption:0 · commitment:0 · confirmation:6
Enthusiasm latest 9 / prev 8 (rising)
FIS's AI thesis evolved from a broad "strategic accelerant" framing (systems-of-record moat, unified data stack, agentic commerce/fraud) to a concrete go-to-market architecture: a governed data-and-AI platform, "orchestrated intelligence," and a co-built Anthropic financial-crimes agent FIS owns and distributes. Management enthusiasm rose materially, but credibility on near-term P&L impact is limited—they explicitly excluded agent revenue from 2026 guidance and offered no hard FIS-specific savings or bookings numbers, only industry TAM, data-scale, qualitative Copilot productivity, and a days-to-minutes client outcome target.
GROUNDED NEXT-FY IMPACT vs CONSENSUS
Grounded on actual base — revenue $10.7B · net income $0.4B · net margin 3.6% · diluted EPS 0.75
These are next-fiscal-year annual uplift estimates, not next-quarter numbers.
Aggregate next-FY est. rev uplift: 0.0% · next-FY EPS uplift: 0.0% · vs analysts: inline · priced in: low (model's call-read: high; verdict above is the hard-data one used for ranking) · confidence: 4/10
| Claim | Figure | Arithmetic | Next-FY Rev % | Next-FY EPS % |
|---|
Agent revenue in FY2026 = zero / not contemplated
revenue | $0 in FY2026 guide | Mgmt explicit: 'Nothing is contemplated in our guide in 2026... revenue take shape in 2027.' Next-FY AI revenue = 100 × $0 / $10,677M = 0.0%; incr_NI = $0 → eps_uplift = 0.0% vs adjusted NI (not GAAP $382M). | 0 | 0 |
Financial crimes TAM $35–40B (first agent)
revenue · soft | $35B–$40B industry spend | Industry TAM only; FIS capture % undisclosed AND mgmt says $0 converts in FY2026. Illustrative 1% of $37.5B midpoint = $375M = 3.5% of rev, but explicitly NOT in next FY → next-FY figure $0, FIS share unanchored. | | |
Internal Copilot engineering productivity
productivity · soft | 'significant' (no numeric) | No $ or FTE base disclosed; directionally bottom-line but cannot size after-tax EPS. | | |
4x'ed data/AI investment
cost · soft | 4x investment | Input spend multiplier with no prior $ base; not an output revenue/cost figure. Cannot compute Δopex or EPS without inventing a base. | | |
Industry AI adoption 8x vs 2023
engagement · soft | 8x | Industry-wide adoption trend, not FIS revenue/$; no FIS share or conversion disclosed → not mappable to $10,677M. | | |
Investigation time days→minutes
productivity · soft | days to minutes | Client-side (bank's cost) outcome, not FIS P&L; supports pricing power but no FIS $ anchor. | | |
73B transactions / ~1.1B accounts (data moat)
other · soft | 73B txns; ~1.1B accounts | Scale/moat enabler, not a revenue or cost figure; no monetization rate disclosed. | | |
Margin: AI a 'significant lever' / integration synergies
cost · soft | no bps disclosed | Qualitative margin lever; no AI-specific basis points or $ operating-income lift to apply to $1,759M op income / $10,677M revenue; not separable from integration synergies. | | |
Assumptions: Next fiscal year = FY2026 (ended 2026-12-31). Mgmt explicitly places agent revenue at $0 in FY2026 guide ('nothing in the guide'); first commercial agents back-half 2026, revenue 'takes shape' 2027 — entire quantifiable AI revenue is FY2027+, outside next FY. EPS sized on ADJUSTED base (FY2025 consensus NI ~$2,998.9M, EPS $5.77, ~28% net margin); GAAP $382M / $0.75 EPS (3.6% margin) is one-off-distorted and would inflate EPS% artifacts. Tax 21% reserved for any $ opex savings (none sized). If/when future agent revenue is sized it is high-margin software (~70% incremental margin), but that is a FY2027 exercise; GAAP net margin 3.58% would be the floor if margin unspecified. Bookings vs revenue: no bookings $ disclosed; TAM is industry spend, not FIS bookings. FIS is purely an ADOPTER — no AI-compute/infrastructure supplier line.
Top line: Zero for the next fiscal year by management's own guidance: 'Nothing is contemplated in our guide in 2026... revenue really take shape in 2027.' Sole hard claim = $0 agent revenue → 0.0% of $10.677B base. The $35–40B financial-crimes TAM, 8x industry adoption, and 73B-txn data moat frame a real 2027+ opportunity but are unanchored to FIS dollars without invented capture rates. Consensus FY2026 revenue $13.797B implies +30.0% vs FY2025 consensus $10.607B ((13,796,619,157 − 10,607,039,256)/10,607,039,256×100 = 30.05%) — that uplift is portfolio/deal-driven, NOT attributable to quantified AI agent revenue. Next-FY adopter rev uplift = 0.0%.
Bottom line: No quantified FY2026 EPS contribution. 4x investment, Copilot 'significant' productivity, and AI/integration margin leverage are directionally accretive but carry no disclosed $ or bps base → null, not zero-with-confidence. Hard FY2026 agent revenue = $0 → 0.0% EPS uplift on adjusted NI (~$3.0B). GAAP NI margin 3.58% (denominator $382M) would make even modest $ savings look like huge GAAP EPS%; ignored for headline EPS%. Next-FY EPS uplift = 0.0%.
Adopter-side aggregate next-FY uplift: 0.0% rev / 0.0% EPS (sole hard claim). Consensus FY2026 revenue $13,797M (+30.0% vs FY2025 est $10,607M) and EPS $6.28 (+8.7% vs $5.77) is portfolio/deal-driven, and mgmt states AI is $0 in the 2026 guide, so consensus carries no AI contribution either — AI uplift is inline-with-zero for the next FY (no gap to trade). Genuine 2027+ agent optionality (single-digit % of a $35–40B TAM on a $10.7B base could be material) is unmodeled, but it forces NO revision above FY2026 consensus; an illustrative 1% capture = $375M = 3.5% of rev is explicitly NOT used (soft). Actual FY2025 revenue $10.677B is +0.66% vs consensus $10.607B.
MODEL CONSENSUS (impact)
partial
Full agreement on $0 next-FY AI rev/EPS, adopter side, adjusted EPS basis, inline. Differed on priced_in and confidence; resolved conservatively.
Conflicts reconciled
- priced_in: X=high vs Y=low -> used high (more conservative/less optimistic): the only hard claim ($0) is fully consistent with consensus carrying no AI, so no near-term upside to capture; Y's 'low' rests on unmodeled 2027+ optionality, not a next-FY beat
- confidence: X=4 vs Y=7 -> used 4, lowered per conflict-resolution rule
| Field | Opus 4.8 | GPT-5.5 |
|---|
| Rev uplift % | 0 | – |
| EPS uplift % | 0 | – |
| Priced in | low | – |
| vs analysts | inline | – |
| Confidence | 7 | – |
| Top line | Zero for the next fiscal year by management's own guidance: 'Nothing is contemplated in our guide in 2026... we'd expect to see revenue really take shape in 2027.' The $35–40B financial-crimes TAM and 73B-transaction data moat frame a real 2027+ opportunity, but FIS's capture is unquantified and explicitly $0 in FY2026. Next-FY adopter rev uplift = 0.0%. | – |
| Bottom line | No quantified FY2026 EPS contribution. Copilot productivity ('significant') and AI margin leverage ('a significant lever') are directionally accretive but carry no disclosed $ base, so they cannot be sized — null, not zero-with-confidence. On the adjusted base ($3.0B NI), even an illustrative $100M after-tax internal saving would be only ~3% of NI, but nothing is anchored. Next-FY EPS uplift = 0.0%. | – |
| Reasoning | Consensus FY2026 revenue $13,797M vs FY2025 $10,607M (+30%) and EPS $6.28 vs $5.77 (+8.7%) — but that growth is portfolio/deal-driven, NOT AI; management states AI is $0 in the 2026 guide, so consensus carries no AI contribution either. Hence the AI uplift is inline-with-zero for FY2026 (no gap to trade in the next FY). priced_in is LOW only in the sense that the genuine 2027+ agent optionality (single-digit % of a $35–40B TAM on a $10.7B revenue base could be material) is in NO 2026 number and largely absent from 2027 estimates too — pure unmodeled optionality, not a quantifiable near-term beat. | – |
Rows highlighted where the two models disagreed.
QUANTIFICATIONS
Data and AI investment multiplier: 4x (Past action (referenced Q4 FY2025), bottomline)
“We 4x'ed our investment in data and AI transformation, unifying our data stack, deploying agents that drive real client outcomes and building domain-specific AI capabilities.”
Industry AI adoption vs. 2023: 8x (Current industry trend (Q4 FY2025), topline)
“AI adoption is accelerating to 8x 2023 levels, and banks recognize AI isn't a future opportunity, it's a competitive imperative today.”
Financial crimes industry spend (TAM context for first agent): $35 billion to $40 billion (Industry today (Q1 FY2026), topline)
“Financial crimes, one of the most urgent and costly challenges for banks today, with an estimated $2 trillion in illicit funds moving through the global financial system, creating a $35 billion to $40 billion spend across the industry.”
Financial crimes agent investigation time reduction (client outcome target): days to minutes (Forward product capability (Q1 FY2026), both)
“The co-built agent will automate the evidence gathering and analysis, reducing investigation time from days to minutes.”
Proprietary transaction/account data scale (AI moat enabler): 73 billion annual payment transactions; approximately 1.1 billion accounts on file (Current (Q1 FY2026), topline)
“We sit on 73 billion annual payment transactions across approximately 1.1 billion accounts on file.”
Agent revenue in FY2026 outlook: zero / not contemplated (FY2026 guidance (Q1 FY2026), topline)
“Nothing is contemplated in our guide in 2026. ... we'd expect to see revenue really take shape in 2027. So nothing in the guide currently.”
Internal engineering productivity from Copilot: significant amount (no numeric) (Current ongoing (Q1 FY2026), bottomline)
“We implemented Copilot last year and are continuing to see a significant amount of productivity lifts in our engineering organization, we're really using that to continue to fuel new product development.”
Margin outlook AI contribution: no AI-specific basis points disclosed (Forward FY2026 (Q4 FY2025), bottomline)
“AI is a significant lever going forward, and we will capture integration synergies over the coming months and years.”
PAST (realized)
- Q1 FY2026 | Stephanie Ferris: We implemented Copilot last year and are continuing to see a significant amount of productivity lifts in our engineering organization.
- Q1 FY2026 | Stephanie Ferris: We've already used AI to make the conversion from one core to another go faster.
- Q4 FY2025 | Stephanie Ferris: We 4x'ed our investment in data and AI transformation, unifying our data stack, deploying agents that drive real client outcomes and building domain-specific AI capabilities.
- Q4 FY2025 | Stephanie Ferris: Other recent launches include SmartBasket, a real-time AI-powered solution that analyzes shopping behavior to automatically apply optimal payment methods, personalized rewards and targeted promotions at checkout.
- Q4 FY2025 | Stephanie Ferris: Demonstrating the power of this combined data even before the transaction closed, we started working with a large regional bank to grow their credit card portfolio, combining core data from FIS and credit transaction data from total issuing solutions, together into a model, enabling the bank to increase their consumers' credit limit.
- Q4 FY2025 | Stephanie Ferris: Leveraging [ DWA's ] AI capabilities, the acquisition strengthens our competitive position across the buy and sell-side compliance space, empowering our clients to make millions of accurate regulatory decisions across global jurisdictions.
CURRENT (now)
- Q1 FY2026 | Stephanie Ferris: This is a deeply collaborative model with Anthropic's forward-deployed engineers embedded alongside ours to design and build this agent from the ground up and enable us to scale them across our platform.
- Q1 FY2026 | Stephanie Ferris: BMO and Amalgamated Bank are our design partners for this first agent.
- Q1 FY2026 | Stephanie Ferris: The feedback we are getting is the announcements we made are exactly where they're looking to spend money. ... And then the other thing I would say is AI.
- Q1 FY2026 | Stephanie Ferris: We have been spending a bunch of time internally as well in our own client contact center and how do we think about, first and foremost, creating and delivering a better client experience, but also getting after productivity and workflows that are our own internal process workflows.
- Q4 FY2025 | Stephanie Ferris: We're spending a bunch of time working with our FIs to figure out how do we update those models for new ways of fraud using Agentic commerce.
- Q4 FY2025 | Stephanie Ferris: I've never seen banks want to or start to adopt technology faster than they're adopting AI.
FORWARD (guidance)
- Q1 FY2026 | Stephanie Ferris: We would expect this agent and then other agents to be in the market in the back half of 2026.
- Q1 FY2026 | Stephanie Ferris: Nothing is contemplated in our guide in 2026. ... we'd expect to see revenue really take shape in 2027.
- Q1 FY2026 | Stephanie Ferris: This is the beginning of a broader road map of purpose-built agents across the banking life cycle, spanning credit decisioning, deposit retention, customer onboarding and fraud prevention available to our clients through a single governed data and AI platform.
- Q1 FY2026 | Stephanie Ferris: This same architecture and the same orchestration is what will power every agent that follows across credit, fraud, onboarding and beyond, regardless of the LLM model used.
- Q1 FY2026 | Stephanie Ferris: We will now be adding agents [to pricing strategy].
- Q4 FY2025 | James Kehoe: AI is a significant lever going forward, and we will capture integration synergies over the coming months and years.
- Q4 FY2025 | Stephanie Ferris: You would expect them to be building their own agents on top of that [FIS real-time data]. ... we are working with them to build out agents that are embedded inside the core and the transaction platforms so that they can reduce their operational costs in the back office.
TRACK RECORD — PROMISE vs DELIVERY
25/100 track record over-promises 6 calls reviewed
Across six calls FIS talks extensively about AI but rarely commits to numbered targets; the one clear dated product milestone—Banker Assist by year-end 2025—was never confirmed and appears quietly shelved, while softer 2025 rollout language and post-close Agentic Commerce were partially or fully met.
Launch Banker Assist agentic AI platform for commercial banking by year-end 2025 — promised Q2 FY2025
quietly-dropped Q2 said 'on track' for year-end launch after strong Emerald conference interest, but Q3–Q4 FY2025 and Q1 FY2026 never confirmed a launch and pivoted narrative to TreasuryGPT upgrades, SmartBasket, Amount, and Anthropic financial-crimes agents instead.
Multiple AI pilots plus additional AI product announcements throughout 2025 — promised Q2 FY2025
partial Management did announce and ship several AI-touched products in 2025—TreasuryGPT July upgrade, Amount acquisition with 7 then 22 deals, SmartBasket, and post-close Agentic Commerce—but without the specific pilot count or announcement cadence implied.
TreasuryGPT upgrade with enhanced risk reporting and liquidity management tools in July 2025 — promised Q2 FY2025
delivered Same call stated the July upgrade was underway/completed and Q3 FY2025 continued citing TreasuryGPT as a live AI treasury tool with no walk-back.
Financial crimes investigation agent (Anthropic co-build) to cut case investigation time from days to minutes and materially lower cost per case — promised Q1 FY2026
too-early Announced with BMO and Amalgamated Bank as design partners; no quantified before/after metrics or general-availability date yet in subsequent calls in this set.
Industry-first AI transaction platform for Agentic Commerce on day one after Issuer Solutions close — promised Q4 FY2025
delivered Management reported announcing the Agentic Commerce platform on the first day after completing the Issuer Solutions acquisition, consistent with the stated milestone.
PRICED-IN (REFINED)
LOW (room left)Est. revisions flat · Fwd P/E 8.1 · EV/Sales 2.2x
AI claim maps to Banking Solutions, Capital Market Solutions
Analyst sentiment is mixed: holds drifted down (11 to 9) while strong buys eased (5 to 4), but price targets clearly stepped down (last-month $55.5 vs last-quarter $57 vs last-year $66.8), so revision momentum is not clearly rising. Forward P/E ~8.1x and EV/Sales ~2.2x are depressed versus typical mature IT/services peers, so the market is not paying up for growth. AI-driven efficiency and revenue uplift would most plausibly land in Banking Solutions and Capital Market Solutions, yet cheap multiples plus flat/falling PT momentum imply that AI upside is not yet embedded in the price.
COVERAGE — ENTHUSIASM TRAJECTORY + CATALYSTS
3Q4 FY20242Q1 FY20256Q2 FY20258Q3 FY20258Q4 FY20259Q1 FY2026
AI enthusiasm across 6 calls — trend ↗ rising
AI went from absent to named products, data moat, agentic commerce, then Anthropic co-built agents on a governed platform.
RECENT AI CATALYSTS & NEWS
BUSINESS IMPACT - QUALITATIVE MATERIALITY
7/10 qualitative impact material medium-term · mixed evidence
Where AI matters: Banking Solutions AI agents & governed data platform
FIS is shipping real AI products (TreasuryGPT, SmartBasket, Agentic Commerce) and co-building owned financial-crimes agents on a regulated data platform, but management explicitly excluded agent revenue from FY2026 guidance and offers no FIS-specific bookings or savings numbers beyond qualitative Copilot productivity.
Caveats: Zero agent revenue in FY2026 guide despite high AI narrative; prior Banker Assist timeline quietly dropped; Anthropic or other frontier-AI partners could use FIS distribution then compete directly for bank AI spend; AI may compress implementation and professional-services hours without proportional recurring license uplift
AI DISRUPTION / CANNIBALIZATION RISK tailwind · 3/10
FIS monetizes sticky systems-of-record, payment rails, and compliance infrastructure—not billable labor arbitrage—so AI mainly augments platform value and client outcomes rather than commoditizing the core product, though horizontal LLM vendors partnering today could eventually disintermediate some middleware and services layers.
OPTIONS / MARKET STRUCTURE
option liquidity: good
proxy inputs — dollar-ADV $277M · beta 0.83 · px $42.52
source: proxy (no options chain on FMP)
FMP /stable/ exposes no options-chain endpoint on this key, so ATM IV, bid-ask spread and open interest are unavailable. Liquidity below is a PROXY from dollar-ADV, beta and price level (a stand-in for option depth), not measured option-market data.
CONFIRMATION — INSIDERS · 13F · LANGUAGE
Confirming — insiders buying, institutions adding, management language 6/10 measured.
INSIDERS buying 3 open-market buy(s) vs 0 sell(s) — net accumulation
INSTITUTIONS (13F) adding as of 2026-03-31: 123 new / 178 closed positions; 511 increased / 313 reduced; institutional ownership -0.27pp; -60 net 13F holders
MGMT LANGUAGE 6/10 measured Firm ownership and agent metrics; first-agent roadmap and goals, no AI revenue figures yet.
commit “FIS owns the agent and the regulated infrastructure, everything that is deployed to the bank.”
commit “reducing investigation time from days to minutes”
commit “We own it and we distribute it.”
VERBATIM AI QUOTES
“There, we announced an industry-shaping agreement with Anthropic, a first of its kind in financial services, which will usher in an entirely new era of modern banking. We also unveiled a number of new solutions, including our new data and AI platform, our new digital asset platform, Lyriq, and Project Keystone, our new tokenized deposit bank-owned network that includes 5 U.S. banks.”
— Stephanie Ferris, Q1 FY2026
“The most important innovations in financial technology right now, AI, digital currency, data, are all running through FIS.”
— Stephanie Ferris, Q1 FY2026
“And now the FIS data and AI platform brings those datasets together as fuel for a real-time AI-native future.”
— Stephanie Ferris, Q1 FY2026
“And now we have built a trust-by-design agentic architecture for what comes next.”
— Stephanie Ferris, Q1 FY2026
“Unlike horizontal data platforms, FIS brings the regulated infrastructure, system of record data and compliance architecture that makes AI deployable in banking today.”
— Stephanie Ferris, Q1 FY2026
“What makes this different from a vendor relationship is what it will deliver: co-built financial crimes agent for financial institutions, combining Anthropic's frontier AI capabilities with FIS' scale, data and regulatory expertise, with the engineering knowledge transferring to FIS to build the agents that come next.”
— Stephanie Ferris, Q1 FY2026
“To be clear, FIS owns the agent and the regulated infrastructure, everything that is deployed to the bank. Anthropic provides the underlying LLM model. Client delivery remains fully owned and protected by us.”
— Stephanie Ferris, Q1 FY2026
“The co-built agent will automate the evidence gathering and analysis, reducing investigation time from days to minutes.”
— Stephanie Ferris, Q1 FY2026
“This is the beginning of a broader road map of purpose-built agents across the banking life cycle, spanning credit decisioning, deposit retention, customer onboarding and fraud prevention available to our clients through a single governed data and AI platform.”
— Stephanie Ferris, Q1 FY2026
“Orchestrated intelligence is how we describe the way FIS brings AI to life in banking as a coordinated architecture where models, data, governance and workflow operate together in a way no single technology provider can replicate alone.”
— Stephanie Ferris, Q1 FY2026
“Anyone can deploy an AI model. What's hard and what FIS uniquely provides is the orchestration layer, connecting Frontier AI to system of record data, routing it through bank-grade compliance infrastructure and delivering it through the trusted relationships we have with financial institutions and regulators around the world.”
— Stephanie Ferris, Q1 FY2026
“We implemented Copilot last year and are continuing to see a significant amount of productivity lifts in our engineering organization, we're really using that to continue to fuel new product development.”
— Stephanie Ferris, Q1 FY2026
“We've already used AI to make the conversion from one core to another go faster.”
— Stephanie Ferris, Q1 FY2026
“Third, emerging technology, particularly AI, is moving from experimental to mainstream at unprecedented speed. AI adoption is accelerating to 8x 2023 levels, and banks recognize AI isn't a future opportunity, it's a competitive imperative today.”
— Stephanie Ferris, Q4 FY2025
“We believe these advantages translate to a significant opportunity for FIS to deliver differentiated AI solutions, which challengers without comparable data, scale, operational integration, trust or relationship cannot replicate.”
— Stephanie Ferris, Q4 FY2025
“This includes the industry's first AI transaction platform supporting Agentic Commerce, enabling AI agents to make, negotiate and pay for purchases using preapproved payment methods, keeping banks central to those flows.”
— Stephanie Ferris, Q4 FY2025
“Other recent launches include SmartBasket, a real-time AI-powered solution that analyzes shopping behavior to automatically apply optimal payment methods, personalized rewards and targeted promotions at checkout.”
— Stephanie Ferris, Q4 FY2025
“Leveraging [ DWA's ] AI capabilities, the acquisition strengthens our competitive position across the buy and sell-side compliance space, empowering our clients to make millions of accurate regulatory decisions across global jurisdictions.”
— Stephanie Ferris, Q4 FY2025
“We 4x'ed our investment in data and AI transformation, unifying our data stack, deploying agents that drive real client outcomes and building domain-specific AI capabilities.”
— Stephanie Ferris, Q4 FY2025
“AI is a strategic accelerant for FIS with adoption unfolding inside existing platforms, augmenting software to improve automation, decisioning and productivity rather than replacing core systems.”
— Stephanie Ferris, Q4 FY2025
“These include accelerating cost actions, rising leverage from AI, our commercial focus on more profitable ACV and improving product mix and the strong margin profile of total issuing solutions and the related cost synergies.”
— James Kehoe, Q4 FY2025
“AI is a significant lever going forward, and we will capture integration synergies over the coming months and years.”
— James Kehoe, Q4 FY2025
ANALYST QUESTIONS ON AI
Q (Q1 FY2026, Dan Dolev (Mizuho)): On the Anthropic deal, is there anything that makes this partnership unique? ... concern ... that AI players can use these types of arrangements to enter new market and then longer term try to disintermediate the existing players.
A: Anthropic came to us because ... you have to have deep, trusted expertise, regulatory compliance capabilities. ... they're putting their forward-deployed engineers with our teams. We are building together ... These are our agents. They are owned by us. Anthropic gets paid based on the token usage of the agents. We own all of the IP and we own the distribution. ... think of them like a cloud provider.
Q (Q1 FY2026, Dan Dolev (Mizuho)): Is there any revenue contribution from the engagement contemplated in your outlook? And when do you expect that to start? When do you expect the agent to be in the market?
A: We would expect this agent and then other agents to be in the market in the back half of 2026. ... nothing is contemplated in our guide in 2026. We would expect to see revenue come forward as we deploy those agents into the market in '26, but we'd expect to see revenue really take shape in 2027. So nothing in the guide currently.
Q (Q1 FY2026, Tien-Tsin Huang (JPMorgan)): On the bank budget side, what are you hearing? Are budgets fully funded? Is there some paralysis given all the AI momentum and discovery and pilots, et cetera?
A: The underlying market is strong, which means the technology budgets are really strong. ... And then the other thing I would say is AI. ... they are looking -- and you're looking for us to help you really think about how do you deploy it in a regulated way? How is it auditable? How is it traceable?
Q (Q1 FY2026, Tien-Tsin Huang (JPMorgan)): Is the line on insourcing versus outsourcing the agent going to be similar to the threshold you see for larger banks choosing to outsource their core account processing?
A: No, banks are looking for us to develop the agents and deploy for them. They're not looking to own agents themselves. ... they're very happy to leverage our agents. It's faster to market for them, cheaper, et cetera.
Q (Q1 FY2026, Jason Kupferberg (Wells Fargo)): Talk a little bit more about the go-to-market strategy and maybe some of the economics behind the partnership [Anthropic]. ... is FIS paying Anthropic at all for access to their engineers or any of the knowledge transfer involved?
A: We have a very exciting enterprise agreement with Anthropic. ... us jointly where they're contributing their forward-deployed engineers with us to build agents. ... they are FIS' agents as we deploy them. We will price them like all agents are priced. We have to work that out still. ... the value to Anthropic ... is really obviously to get more financial institutions using the agent and using their LLM and ultimately taking on the value of their LLM through tokens.
Q (Q1 FY2026, Andrew Schmidt (KeyBanc)): Broader question, just on AI in terms of organizational structure, productivity. ... Curious how you think about this and the opportunity to sort of accelerate product velocity and streamline processes.
A: We implemented Copilot last year and are continuing to see a significant amount of productivity lifts in our engineering organization. ... We are planning the forward-deployed engineers that we're getting from Anthropic where we learn shoulder to shoulder. Not only will we be using those learnings for new agents and new product development, we'll also be using those learnings and putting them into our back office.
Q (Q1 FY2026, Timothy Chiodo (UBS)): The potential for AI to reduce [core] stickiness. ... if AI were able to make the transition from one core to another either quicker or less effort or less risk.
A: We've already used AI to make the conversion from one core to another go faster. ... Haven't yet seen an actual AI-enabled full core that a bank could use. ... I'm not going to sit on the call and tell you that I don't think there's places where AI could make the switching cost less. ... I don't see a full takedown though of like, oh, AI has come and all of a sudden, the cost is gone.
Q (Q4 FY2025, Tien-Tsin Huang (JPMorgan)): How you think about the risk that AI could automate or replace some of the key functions that FIS currently provides to banks, just thinking about the surround products, core banking itself?
A: We actually view AI as a strategic accelerant for our business. ... AI agents make systems of record more valuable. ... we're 4x'ing our investment in data and AI ... deploying agents inside our existing systems and on top and building domain-specific AI capabilities. ... focused on ... fraud prevention, how to predict the next best deposit and lending account ... onboard clients more efficiently ... helping them with productivity initiatives ... we really think it's a strategic accelerant for us versus risk.
Q (Q4 FY2025, Andrew Schmidt (KeyBanc)): On Agentic or GenAI solutions, what are customers actually asking for? And maybe you could just talk about the opportunity for FIS to be a conduit versus other third parties coming in and providing different workflows?
A: Where we're focused ... has been ensuring that when the bank -- when we receive the Agentic transaction on behalf of a bank that we can identify it as an agent working on your behalf ... And then the second is really starting to work with financial institutions to help them think through Agentic fraud. ... we're spending a bunch of time working with our FIs to figure out how do we update those models for new ways of fraud using Agentic commerce.
Q (Q4 FY2025, Vasu Govil (KBW)): How much engagement are you seeing from bank clients today on deploying AI solutions? ... whether it's coming from the largest banks, the mid-sized banks? And how quickly do you think we will start to actually see traction and sort of flowing into the P&L?
A: I've never seen banks want to or start to adopt technology faster than they're adopting AI. ... whether you're a small bank or you're a big bank, you're thinking a lot about it. ... For the most part, banks are really using AI to take down their back office costs. ... that's in compliance and regulatory areas ... And the other place that they're spending a bunch of time on is in fraud because AI does help models become more predictive. [Transcript truncated mid-answer.]