How AICompass Works
AICompass delivers causal analysis across three distinct pillars: Product Fitment, Investment Analysis, and Portfolio Analysis. Each has a different objective and report.
Three Analyses: Objectives & Results
1. Product Fitment Analysis
Objective:
Should we buy this product for our company function? Whether the product can be purchased and implemented for organizational use.
Results:
Strategic Alignment, Technical Fit, Regulatory Readiness, Risk Assessment, Fit Recommendation, Board Memo.
Run Product Fit →2. Investment Analysis (CVC)
Objective:
Corporate Venture Capital perspective: Should we invest in this AI vendor as an equity investment? Not about buying the product — about investing in the company.
Results:
CVC Investment Recommendation, Vendor Product Traction, Investment Return Scenarios, Evaluation Factors.
Run Investment Analysis →3. Portfolio Analysis
Objective:
How will our portfolio behave under macro shocks? Causal stress testing.
Results:
Causal Diversification Score (CDS), Crisis Drawdown, Stress Scenarios (FFR, VIX, Stagflation).
Run Portfolio Analysis →Fit Analysis Modules (Product Fitment)
The Product Fitment Analysis combines your institution profile with vendor data to produce: Strategic Alignment, Technical Fit, Regulatory Readiness, Risk Assessment, and a Fit Recommendation. Each module produces scores and explanations tailored to your institution's size, systems, and maturity.
1. Strategic Alignment
How well the vendor matches your strategic priorities
How it's calculated
The overall score combines four sub-scores with these weights:
- Priority Match (30%) — Does the vendor's category align with your primary strategic priority? (e.g., Member Experience + Conversational AI = high match)
- Member Relevance (30%) — Based on your digital adoption rate, average member age, and product mix
- Timing Appropriateness (20%) — Your AI maturity and implementation experience vs. vendor complexity
- Board Readiness (20%) — Board AI literacy and governance policy vs. vendor transparency
Score interpretation
70+ = Strong alignment; 50–69 = Partial; <50 = Misalignment or timing concerns
2. Technical Fit
Can you technically deploy this vendor?
How it's calculated
- Core Integration (35%) — Does the vendor support your core banking system? Production = 90, Beta = 75, Unknown = 40–60
- Infrastructure Readiness (20%) — Cloud posture vs. vendor deployment model
- Data Readiness (25%) — Your data quality self-assessment and API gateway
- Team Capacity (20%) — IT staff count vs. typical implementation weeks
Risk levels
Core integration can be Low, Medium, High, or Blocker (no documented compatibility).
3. Regulatory Readiness
Are you and the vendor prepared for regulatory scrutiny?
How it's calculated
- Model Risk Alignment (25%) — MRM framework, SR 11-7 alignment for lending AI
- Fair Lending Compliance (20%) — Adverse action support, bias testing (for lending vendors)
- Vendor Compliance Posture (25%) — Vendor's regulatory score, SOC2, NCUA exam readiness
- Examination Readiness (30%) — Your exam rating and governance vs. vendor readiness
4. Causal ROI Projection
ROI adjusted for your institution — not vendor marketing
Vendor case studies often overstate ROI due to selection bias and lack of control groups. AICompass applies causal inference principles to adjust projections for your institution.
Key concepts
- Confounding factors — Institution size, digital adoption, data quality. Case study FIs may differ from you.
- Heterogeneous treatment effect — The same product may perform differently at your institution.
- Financial scenarios — Conservative, expected, and optimistic projections with payback and 3-year ROI.
Causal credibility (1–5)
5 = RCT or quasi-experimental; 3 = Pre/post without controls; 1 = Marketing claim only
5. Risk Assessment
What could go wrong? (Lower score = higher risk)
- Vendor risk (25%) — Vendor risk flags (concentration, regulatory history, etc.)
- Implementation risk (25%) — Core integration risk level
- Regulatory risk (20%) — Governance gaps
- Strategic risk (15%) — Vendor lock-in, switching costs
- Member impact risk (15%) — Fair lending, bias (for lending AI)
Mitigations are tagged as Immediate, Pre-Launch, or Ongoing.
6. Decision Recommendation
The final verdict
Recommendation logic
- Do Not Proceed — Core integration is a Blocker
- Strong Proceed — Avg alignment ≥75 and risk inverted ≥70
- Proceed with Conditions — Avg ≥65, risk inverted ≥60 (conditions listed)
- Proceed with Caution — Avg ≥50 (pilot recommended)
- Defer — Avg <50 or risk >70
The recommendation includes conditions, prerequisites, and a suggested implementation approach (Pilot, Phased, etc.).
Vendor Database Scores
Scores shown on vendor cards and detail pages
Vendor scores are pre-computed and apply to all institutions. The Fit Analysis combines these with your profile.
- Overall Vendor Score — Technology (25%) + Regulatory (25%) + FI Fit (20%) + Value (15%) + Risk inverted (15%)
- AI Genuineness (1–10) — Real ML vs. rules-based. 10 = proprietary ML, published research; 1 = no real AI
- Technology, Regulatory, FI Fit — Component scores for the vendor's capabilities
Ready to run your fit analysis?
Your institution profile + vendor selection = tailored recommendation
Run Fit Analysis