Algorithms for Inclusion : Data Driven Lending for Women Owned SMEs

Alibhai, Salman ; Achew, Mengistu Bessir ; Coleman, Rachel ; Khan, Anushe ; Strobbe, Francesco

World Bank, Washington, DC

关键词: ACCESS TO FINANCE;    MICROFINANCE;    GENDER EQUITY;    SMEs;    MICROENTERPRISE;   

RP-ID  :  116981

学科分类：社会科学、人文和艺术（综合）

来源: World Bank Open Knowledge Repository

【 摘 要 】

All over the world, women have lessaccess to credit than men. Because of both discriminatoryproperty laws and unwritten social customs, women are lesslikely than men to own high-value assets that can be used ascollateral to secure loans. Financial institutions indeveloping countries rely on heavy collateral requirementsbecause they don’t have enough information about theirborrowers. New technologies - many emerging from financialtechnology (fintech) startups in the Silicon Valley - havethe potential to generate data on borrowers that can replacetraditional collateral requirements, and unlock finance forwomen. In Ethiopia, the authors explored introducing fintechthat can harness the data that financial institutions arealready sitting on. The technology focuses on digitizinghard-copy loan application files of previous borrowers toidentify trends and characteristics associated withrepayment, and predict creditworthiness of new borrowers.Fintech solutions can viably address the collateralconstraint for women borrowers, and can work even in lowtech environments. But technology adoption isn’t easy, andassessing the readiness of financial institutions to adoptfintech and embark on technological change is a criticalfirst step.