DigiAsia Corp (FAASF) Fair Value & Analysis
Technology · US · Market cap $1.8M
Fair value as of: Jun 26, 2026
Analysis
DigiAsia Corp (FAASF) currently trades at $0.0265, while our model-based Fair Value estimate is $0.0566 — implying the stock looks roughly 113.5% undervalued today. We read business quality at 81/100 (high quality), in the Technology sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: low) — always confirm before acting.
About the company
DigiAsia Corp. develops an AI embedded finance platform in Southeast Asia. It offers B2B fintech platform for bill payments, supply chain payments, and branchless banking solutions for merchants, partners, and customers; digital wallets; QRIS Payment as a Service; Cash Management system for ERP system that allows users to create a closed-loop cash management system; Digital Product & Billers, which allows users to purchase digital products or pay bills to multiple issuers or billers; and Remittance Product that enables users to transfer funds from their application cash-to-cash, cash-to-account, account-to-cash, and account-to-account. The company also provides B2B Loan product for micro, small, and medium enterprises; Gold saving products; Virtual Card Number to generate a digital invoice for customers to accept payment over internet transactions; Buy Now Pay Later; e-KYC module to Onboard new customers securely in compliance with regulation; Cash in & Cash Out points; KasPro Bank …
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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.