Maya payment data blind spots

Maya’s lending edge is also its main stress risk, because the same wallet and payment data that lets it approve first time borrowers fast can become less reliable when jobs weaken and cash flows break suddenly. In normal periods, frequent wallet use and regular merchant sales are useful signals. In a downturn, those signals can lag real distress, so losses can show up after credit has already been extended from Maya’s deposit base.

• Maya underwrites Easy Credit and merchant loans from in app behavior, payment frequency, wallet usage, and merchant processing history, not from deep bureau files. That works well for reaching unbanked users, but it means the model is trained mostly on transaction patterns inside Maya’s own ecosystem.
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• The exposure matters because lending has become a much larger part of the bank. Maya disclosed a 42% loan to deposit ratio in 2024, up from 5% in 2022, and said 60% of borrowers use Maya as their only bank. That gives Maya unique reach, but fewer outside data points when borrowers come under pressure.
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• This is a broader digital bank issue in the Philippines, not just a Maya issue. Public reporting on the sector has highlighted weaker asset quality for digital banks, driven by unsecured consumer lending and thin credit files. Maya’s reported 3.8% NPL was better than the segment, but the real test is a full credit cycle with rising unemployment.
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Going forward, the winners in Philippine digital banking will be the ones that turn payment data into lending without mistaking activity for resilience. Maya is well positioned because it controls the wallet, deposits, merchant checkout, and loan offer in one app. The next stage is proving that this closed loop can still price risk correctly when the economy turns down.