Provision Information Co (6590) Fair Value & Analysis
Technology · TW · Market cap 1.7B TWD
Fair value as of: Jun 24, 2026
Analysis
Provision Information Co (6590) currently trades at 68.00 TWD, while our model-based Fair Value estimate is 92.03 TWD — implying the stock looks roughly 35.3% undervalued today. We read business quality at 82/100 (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: medium) — always confirm before acting.
About the company
Provision Information Co.,Ltd. provides payment, financial application, security solutions, and outsourcing services in Taiwan. The company offers payment solutions such as FISC, mobile, TCH, and digital payment solutions; and financial application solutions, such as ProFEP, a shared platform for developing financial transaction; ProComm which provides solutions for internal system connection of banks; ProFCS which offers services of file exchange, schedule handling, and file format exchange between different systems; ProMIPS encryption kit for file transmission; ProACS provides transaction control service for automation machines; ProAMS, that integrates ProACS to provide services of online monitoring, command releasing, and accident circulating; and ProJCIC offers a user-friendly interface to authorized users in banks to inquire JCIC system. It also provides information security service such as ProHSMTM (Hardware Security Module), a financial payment provides payment cryptographic …
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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.