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Good practice Imported

Ehsaas Emergency Cash — Biometric Cashpoints Removing the PIN Barrier for Low-Literacy Women in Pakistan

Pakistan · Islamabad · See the Pakistan profile · See the Islamabad profile

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Facing evidence that low-literacy women struggle with PIN-based ATMs, Pakistan's Ehsaas Emergency Cash paid COVID-19 stipends via fingerprint-only biometric cashpoints instead, reaching ~15 million households, with ~54% of payments going directly to women.

Details

Promoter
Benazir Income Support Programme (BISP) / Poverty Alleviation and Social Safety Division
Period
2020–ongoing
Keywords
social protection, financial inclusion, biometric identification, cash transfers

Description

When Pakistan launched the Ehsaas Emergency Cash programme in April 2020 to cushion the COVID-19 shock, programme designers had to reckon with documented evidence that women with low literacy and numeracy find PIN- and debit-card-based ATMs difficult to operate — a barrier that risked excluding the very women the cash transfer was meant to reach.
The programme's response was to disburse the PKR 12,000 (about $75) stipend exclusively through biometrically-enabled cashpoints — fingerprint verification at ATMs, designated retail shops and temporary payment camps — removing the need to remember a PIN, hold a card, or read a screen, while also ensuring that the registered woman herself, rather than a male relative, had to appear in person to collect the funds.
The World Bank documented that around 53.96% of payments were made directly to women, with 4.526 million women in the existing Kafaalat registry receiving guaranteed payments, and ranked Ehsaas Emergency Cash third globally by population coverage among COVID-19 cash-transfer programmes, reaching roughly 15 million households — about 100 million people.
Independent analysis by the Center for Global Development is candid about the design's limits: because eligibility still depended on owning a mobile phone and a national ID, and only 27.76% of poor women (versus 74.13% of poor men) owned a phone at the time, the biometric design solved the literacy barrier but not the underlying ownership gap — CGD estimated that up to 78% of poor women could be excluded from any future, non-registry-based expansion of a similar biometric-only model, a lesson both institutions flag for the design of future programmes.

Read the full analysis: https://www.bisp.gov.pk/

Implementation

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