American International Group (AIGB34) Fair Value & Analysis
Financial Services · BR · Market cap R$205B
Fair value as of: Jun 24, 2026
Analysis
American International Group (AIGB34) currently trades at R$397.02, while our model-based Fair Value estimate is R$91.86 — implying the stock looks roughly 76.9% overvalued today. We read business quality at 91/100 (high quality), in the Financial Services sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: high).
About the company
American International Group, Inc. provides insurance products for commercial, institutional, and individual customers in North America and internationally. It operates through three segments: North America Commercial, International Commercial, and Global Personal. The company offers commercial and industrial property insurance, including business interruption and package insurance that cover exposure to made and natural disasters; general liability, environmental, commercial automobile liability, workers' compensation, excess casualty, and crisis management insurance products; risk-sharing and other customized structured programs for large corporate and multinational customers; professional liability insurance; and marine, energy-related property insurance products, aviation, political risk, trade credit, and trade finance products. It also provides group personal accident and business travel products for employees, associations, and other organizations; voluntary and sponsor-paid …
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How we calculate Fair Value
Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.
Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.