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Credit Scoring in Financial Inclusion

Statistical models can help lenders in emerging markets standardize and improve their lending decisions. These models define customer scoring based on a statistical analysis of past borrowers’ characteristics instead of relying on the subjective judgments of loan officers. Evidence shows that statistical models improve the accuracy of credit decisions and make lending more cost-efficient. They also help companies make key decisions throughout the customer lifecycle.

Lenders sometimes assume that statistical credit scoring is too costly or difficult or that they do not have the kind of data needed to implement it. However, the primary input needed for this type of modelling is something many providers already possess: customers’ repayment histories. This guide from CGAP explains what types of data lenders can leverage for statistical credit scoring and the ways in which it can be used.

CGAP is a global partnership of more than 30 leading development organizations that works to advance the lives of poor people through financial inclusion. Using action-oriented research, we test, learn and share knowledge intended to help build inclusive and responsible financial systems that move people out of poverty, protect their economic gains and advance broader development goals. We research and experiment to achieve proof of concept and extract lessons that can be built to scale by our partners, who apply our insights in the marketplace.

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