VOL. XCIV, NO. 247
★ WIDE MOAT STOCKS & COMPETITIVE ADVANTAGES ★
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Wednesday, December 31, 2025
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 (credit scoring/related predictive scores) and Software (decisioning and analytics software). The Scores segment benefits from de facto standardization in U.S. lending workflows (including mortgage processes) and backward-compatible score formats that reduce lender switching. The Software segment is sticky due to workflow embedding and multi-year subscription contracts, supported by disclosed ARR and net retention. Key durability risks are FHFA/GSE policy shifts toward lender choice and competing score models, plus competitive displacement and in-house build trends in enterprise software.
Primary segment
Scores
Market structure
Quasi-Monopoly
Market share
90% (reported)
HHI: —
Coverage
2 segments · 6 tags
Updated 2025-12-31
Segments
Scores
Consumer credit scoring and related predictive scores used in lending decisions
Revenue
58.7%
Structure
Quasi-Monopoly
Pricing
strong
Share
90% (reported)
Peers
Software
Decision management and analytics software (risk, fraud, customer management, and decision automation)
Revenue
41.3%
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 FY2025 segment results in the FY2025 Form 10-K: Scores revenue $1,168.575B of total segment revenue $1,990.869B; Scores segment operating income $1,026.243B of total segment operating income $1,273.937B.
De Facto Standard
Network
De Facto Standard
Strength: 5/5 · Durability: medium · Confidence: 4/5 · 3 evidence
Deep institutional standardization in U.S. lending (especially mortgage workflows) reinforces FICO's position as the default score used in many credit decisions.
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: 4/5 · Durability: medium · Confidence: 4/5 · 2 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.
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: 4/5 · Durability: medium · Confidence: 3/5 · 2 evidence
FICO is the most recognized credit score brand in the U.S.; consumer and lender familiarity supports preference and lowers adoption friction.
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 FY2025 segment results in the FY2025 Form 10-K: Software revenue $822.294M of total segment revenue $1,990.869B; Software segment operating income $247.694M of total segment operating income $1,273.937B. FY2025 Software ARR was disclosed as $747.3M and Software dollar-based net retention as 102% (as of Sep 30, 2025).
Data Workflow Lockin
Demand
Data Workflow Lockin
Strength: 4/5 · Durability: medium · Confidence: 4/5 · 2 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.
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: 4/5 · Durability: medium · Confidence: 4/5 · 2 evidence
Retention and multi-year contract norms indicate meaningful switching costs, even as customers remain price-sensitive and can multi-vendor.
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
...a requirement...that U.S. lenders provide FICO Scores for each mortgage delivered...
Shows embedded use in GSE-eligible mortgage delivery workflows.
FICO Scores are used in 90% of U.S. lending decisions...
Company-stated adoption penetration supports a de facto standard claim (U.S. lending decisions).
permit lenders to choose between Classic FICO and VantageScore 4.0...
Confirms that Classic FICO has been the single required model for decades, but policy is shifting toward choice (durability headwind).
...enhancing their compatibility with existing credit underwriting systems and models.
Directly supports workflow compatibility as a source of switching friction.
...generate scores on the same 300-850 scale as standard FICO Scores...
Scale consistency supports standardized interpretation across products/versions.
Showing 5 of 11 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
Curation & Accuracy
This directory blends AI‑assisted discovery with human curation. Entries are reviewed, edited, and organized with the goal of expanding coverage and sharpening quality over time. Your feedback helps steer improvements (because no single human can capture everything all at once).
Details change. Pricing, features, and availability may be incomplete or out of date. Treat listings as a starting point and verify on the provider’s site before making decisions. If you spot an error or a gap, send a quick note and I’ll adjust.