AIG vs IGIC

American International Group, I vs International General Insurance — Valuation Comparison 2026

AIG

Insurance - Diversified
American International Group, I
Quality
7.9
out of 10
Value Trap
23
SAFE
Price
$74.40
Last close
Models
11/13
Active
VS

IGIC

Insurance - Diversified
International General Insurance
Quality
7.7
out of 10
Value Trap
6
SAFE
Price
$24.54
Last close
Models
11/13
Active

Model-by-Model Comparison

ModelType AIG Fair ValueAIG Upside IGIC Fair ValueIGIC Upside
Bayesian DCF Intrinsic $80.12 +7.7% $32.75 +33.4%
Earnings Power Value Intrinsic $198.60 +166.9% $35.85 +41.1%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for AIG vs IGIC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

AIG vs IGIC — Which Stock Is More Undervalued?

AIG scores higher with a 7.9/10 quality rating vs IGIC's 7.7/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing American International Group, I (AIG) and International General Insurance (IGIC) across 13 institutional-grade valuation models reveals how each company's intrinsic value stacks up against its market price. CirclFi's engine processes SEC EDGAR 10-K and 10-Q filings, FRED macroeconomic data, and GDELT news sentiment to generate independent fair value estimates daily.

AIG currently trades at $74.40 with a QOC of 7.9/10, while IGIC trades at $24.54 with a QOC of 7.7/10.

Both companies are analyzed with models spanning intrinsic (Bayesian DCF, EPV), scenario-based (First Chicago), regime-switching (Markov DDM, RCMH-DCF), machine learning (ML-RIV, FTNN Topology), and ensemble methods (CUCE).