★ WIDE MOAT STOCKS & COMPETITIVE ADVANTAGES ★
VOL. XCIV, NO. 247
Fair Isaac Corporation
FICO · New York Stock Exchange
Weighted average of segment moat scores, combining moat strength, durability, confidence, market structure, pricing power, and market share.
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Overview
Fair Isaac (FICO) operates two reportable segments: Scores and Software. In H1 FY2026, Scores produced 64.8% of segment revenue and 85.2% of segment operating income, with Q2 growth driven by mortgage score pricing and origination volume. Scores still benefit from de facto standardization, backward-compatible score formats, and FICO Mortgage Direct License channel optionality, but FHFA now permits approved GSE lenders to choose Classic FICO or VantageScore 4.0. Software remains sticky through decision-workflow embedding, platform ARR growth, and multi-year subscriptions, though enterprise procurement, cloud-native tooling, and in-house models remain pressure points.
Primary segment
Scores
Market structure
Quasi-Monopoly
Market share
90% (reported)
HHI: —
Coverage
2 segments · 6 tags
Updated 2026-07-01
Segments
Scores
Consumer credit scoring and related predictive scores used in lending decisions
Revenue
64.8%
Structure
Quasi-Monopoly
Pricing
strong
Share
90% (reported)
Peers
Software
Decision management and analytics software (risk, fraud, customer management, and decision automation)
Revenue
35.2%
Structure
Competitive
Pricing
moderate
Share
—
Peers
Moat Claims
Scores
Consumer credit scoring and related predictive scores used in lending decisions
Revenue share and operating profit share computed from H1 FY2026 segment results in the March 31, 2026 Form 10-Q: Scores revenue $779.507M of total segment revenue $1.203636B; Scores segment operating income $700.329M of total segment operating income $821.537M. Agreements with the three nationwide consumer reporting agencies generated 58% of H1 FY2026 total revenue.
De Facto Standard
Network
De Facto Standard
Strength
Durability
Confidence
Evidence
Deep institutional standardization in U.S. lending reinforces FICO's default-score position; direct mortgage licensing improves channel optionality, while FHFA/GSE policy now allows Classic FICO or VantageScore 4.0 for approved lenders.
De Facto Standard moat: definition, examples, and stocks
Erosion risks
- FHFA/GSE policy enabling competitor models (e.g., VantageScore 4.0) or multi-model regimes
- Fee/pricing scrutiny or regulation in mortgage credit scoring
- Large lenders increasing reliance on internal underwriting models using alternative data/AI
Leading indicators
- FHFA/Fannie Mae/Freddie Mac selling guide and delivery-policy updates on accepted score models
- Mix of score models used in GSE deliveries (Classic FICO vs alternatives)
- Mortgage-related score volumes vs mortgage origination cycles
Counterarguments
- Credit bureaus' VantageScore can gain share where it becomes fully operationally accepted
- Major lenders can multi-home across models and reduce reliance on any single vendor
Format Lock In
Demand
Format Lock In
Strength
Durability
Confidence
Evidence
Backwards-compatible score scales and stable score-to-risk relationships reduce change costs for lenders and keep legacy underwriting processes usable across model versions.
Format Lock In moat: definition, examples, and stocks
Erosion risks
- Multi-model requirements reduce reliance on one score format
- Middleware/decision engines make it easier to swap scoring inputs
Leading indicators
- Operational requirements to submit multiple score models in major channels
- Adoption timelines for FICO 10T or other next-gen models vs Classic
Counterarguments
- Lenders can map between score scales and recalibrate models; switching may be manageable
- If regulators mandate change, compatibility becomes less protective
Brand Trust
Demand
Brand Trust
Strength
Durability
Confidence
Evidence
FICO is the most recognized credit score brand in the U.S.; consumer and lender familiarity supports preference and lowers adoption friction.
Brand Trust moat: definition, examples, and stocks
Erosion risks
- Consumer confusion from multiple score brands/models reduces FICO's mindshare advantage
- Credibility damage from perceived pricing or fairness controversies
Leading indicators
- Consumer adoption of non-FICO scores in credit monitoring and lender experiences
- Brand search interest / consumer awareness metrics (if available)
Counterarguments
- Many borrowers do not know which score a lender uses; brand may matter less in underwriting
- Credit bureaus can promote their own scoring brands through consumer channels
Software
Decision management and analytics software (risk, fraud, customer management, and decision automation)
Revenue share and operating profit share computed from H1 FY2026 segment results in the March 31, 2026 Form 10-Q: Software revenue $424.129M of total segment revenue $1.203636B; Software segment operating income $121.208M of total segment operating income $821.537M. Q2 FY2026 Software revenue increased 7%; Software ARR rose 10% year over year, with Platform ARR up 49% and total Software DBNRR at 109%.
Data Workflow Lockin
Demand
Data Workflow Lockin
Strength
Durability
Confidence
Evidence
FICO's software is embedded in high-stakes decision workflows (fraud, onboarding, credit risk), creating operational switching costs once data, rules, and models are in production.
Data Workflow Lockin moat: definition, examples, and stocks
Erosion risks
- Customers consolidate onto hyperscaler-native data/ML stacks and build in-house decisioning
- Implementation complexity or platform migration risk reduces renewal/expansion
- Competitive displacement by broader suites (core banking, CRM, cloud platforms)
Leading indicators
- Software ARR growth rate
- Dollar-based net retention rate
- Platform ARR mix vs non-platform ARR
Counterarguments
- Large enterprises can replace decisioning tools by standardizing on general-purpose ML/feature stores
- Best-of-breed vendors and internal teams can replicate parts of the stack, weakening lock-in
Switching Costs General
Demand
Switching Costs General
Strength
Durability
Confidence
Evidence
Retention and multi-year contract norms indicate meaningful switching costs, even as customers remain price-sensitive and can multi-vendor.
Switching Costs General moat: definition, examples, and stocks
Erosion risks
- Procurement pressure forces price concessions at renewal
- Security incidents or reliability issues accelerate competitive replacement
- Open-source and cloud-native tooling lowers switching costs over time
Leading indicators
- NRR trend (up/down)
- Renewal rates and churn in non-platform vs platform products
- Gross margin impact from hosting and delivery costs
Counterarguments
- Enterprises can run parallel systems and migrate in phases; switching is possible
- Some buyers treat decisioning tools as interchangeable modules if data is centralized
Evidence
used in most U.S. credit decisions
Annual report describes FICO Scores as a standard measure of consumer credit risk in the U.S.
used by 90% of top U.S. lenders
Company-stated adoption penetration supports a de facto standard claim among large U.S. lenders.
choose between two approved credit score models
Confirms Classic FICO remains approved, but GSE mortgage deliveries are moving to an interim lender-choice regime.
primarily attributable to a higher mortgage origination scores unit price
Q2 FY2026 Scores growth was driven by higher B2B unit price and mortgage origination volume, supporting pricing power.
tri-merge resellers have the option to calculate and distribute FICO Scores directly to their customers
Shows FICO adding a direct route around credit-bureau distribution for mortgage scores.
Showing 5 of 13 sources.
Risks & Indicators
Erosion risks
- FHFA/GSE policy enabling competitor models (e.g., VantageScore 4.0) or multi-model regimes
- Fee/pricing scrutiny or regulation in mortgage credit scoring
- Large lenders increasing reliance on internal underwriting models using alternative data/AI
- Multi-model requirements reduce reliance on one score format
- Middleware/decision engines make it easier to swap scoring inputs
- Consumer confusion from multiple score brands/models reduces FICO's mindshare advantage
Leading indicators
- FHFA/Fannie Mae/Freddie Mac selling guide and delivery-policy updates on accepted score models
- Mix of score models used in GSE deliveries (Classic FICO vs alternatives)
- Mortgage-related score volumes vs mortgage origination cycles
- Operational requirements to submit multiple score models in major channels
- Adoption timelines for FICO 10T or other next-gen models vs Classic
- Consumer adoption of non-FICO scores in credit monitoring and lender experiences
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