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EBITDA
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What the record says.
Apollo Finvest reported a slight dip in revenue, PAT, and AUM for FY26 as it transitions from term loans to its flagship digital lending product, Apollo Cash. The retail book share rose to 51% of AUM from 24% previously, driven by scaling co-lending partnerships and the new app. Apollo Cash, launched in January 2026, has already disbursed ₹5+ crore across 19,000+ pin codes with zero marketing spend, achieving 1 lakh+ downloads and 18,000+ loans. Monthly disbursements grew 300% from February to April. Management targets ₹50 crore total disbursements in the first year, with Apollo Cash contributing 15-20% of the loan book. The company is heavily investing in data science underwriting to serve underbanked borrowers. Risk: Rapid scaling of unsecured lending could lead to higher delinquencies if underwriting models underperform.
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Guidance to track
- Total disbursements for Apollo Cash in the first year (FY27) are expected to be around ₹50 crore, with AUM of ₹10-15 crore due to churn.
- By end of FY27, Apollo Cash is expected to constitute 15-20% of the total loan book.
- Management expects the retail book (Apollo Cash + co-lending) to become the vast majority of AUM, with term loans reducing.
- Given low debt-to-equity, the company does not expect any external fundraise in the near term.
Risks flagged
- Apollo Cash targets underbanked borrowers with small-ticket unsecured loans; high growth may lead to elevated delinquencies if underwriting models are not yet proven.
- The shift from term loans to retail lending has caused a dip in revenue and PAT; profitability may remain subdued until Apollo Cash scales sufficiently.
- The digital lending space is crowded with well-funded players; Apollo's reliance on organic growth and data science underwriting may face challenges in customer acquisition and retention.
Key quotes
- Our goal is to build out the leadership team... people who basically been in the digital lending space itself at least for a period of 4 to 5 years.
- The more data that you end up gathering the more you can understand... how much is that particular data point important from helping us understand what is a good borrower and what is a bad borrower.
- If a person like you ended up coming to our app it would most likely look to us like a fraud or a very bad credit situation because we don't expect a person like you to come to our app.
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