DiGiSPICE Technologies Limited (DIGISPICE) Fair Value & Analysis
Financial Services · IN · Market cap ₹4.1B
Fair value as of: Jun 29, 2026
Analysis
DiGiSPICE Technologies Limited (DIGISPICE) currently trades at ₹19.68, while our model-based Fair Value estimate is ₹15.46 — implying the stock looks roughly 21.4% overvalued today. We read business quality at 96/100 (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
DiGiSPICE Technologies Limited engages in the provision of tech-enabled local payments network services in India and internationally. Its platform, Spice Money, which offers cash-in/cash-out, mini-ATM transactions, and e-KYC-based account openings to money transfers, credit and loan services, insurance, and bill payments, as well as collection services, and current and savings accounts. The company also provides AePS, CMS, DMT, M-ATM, BBPS, recharges, PAN, and travel services through Adhikaris; and Spice Pay, a PPI wallet. In addition, it offers secured and unsecured lending products in rural and semi-urban markets; and bookings for trains, flights, buses, and hotels through the Travel Union platform. The company was formerly known as Spice Mobility Limited and changed its name to DiGiSPICE Technologies Limited in August 2019. The company was incorporated in 1986 and is based in Noida, India. DiGiSPICE Technologies Limited is a subsidiary of Spice Connect Private Limited.
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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.