Introduction: Why is a gender-lens analysis useful?

The guide highlights that women generally demonstrate better loan repayment performance than men, suggesting that existing credit systems may undervalue women’s creditworthiness. A gender-lens approach enables financial service providers (FSPs) to identify such discrepancies and adjust lending models accordingly. Evidence from developing markets shows women’s lower credit risk is often overlooked due to biases or insufficient gender-disaggregated data. A gender-lens analysis can therefore reveal opportunities to increase financing to women without elevating portfolio risk. While not all lenders currently differentiate by gender, doing so can support women’s financial inclusion and align with sound risk management practices.

How to use this guide

The guide serves as a practical resource for lenders of varying capacities. It progresses from basic gender-lens analysis to more advanced model adjustments. Section one explains how to analyse portfolio data by gender. Section two details gender-intentional credit scoring and usage strategies, while section three introduces gender-intentional scorecard development. Later sections address alternative strategies and implementation. The guide recommends that lenders first understand their existing portfolio’s gender composition and repayment performance, then use this insight to test and refine lending models.

Gender-lens analysis

A basic gender-lens analysis involves comparing loan approval and repayment rates by gender using borrower-level data. Key indicators include the share of women and men borrowers, approval rates, and “bad” loan rates (non-performing loans). For example, if women represent 60% of borrowers but have a 5% “bad” rate compared to men’s 10%, lenders can identify potential to safely expand lending to women. Such analysis reveals where gender-intentional strategies can enhance portfolio growth and inclusivity. Lenders are encouraged to contextualise results by considering social norms, loan conditions, and any subjective bias in credit decisions.

Gender-intentional scorecard usage

Gender-intentional scorecard usage helps lenders apply gender insights to credit decisions. Using data from TymeBank in South Africa, the guide demonstrates that women borrowers had a “bad” rate of 6.4%, compared with 8.1% for men, indicating that women posed lower risk under identical conditions. Applying separate score cut-offs by gender (a “gender-intentional” strategy) could increase overall approvals from 79% to 83% while maintaining a 5% portfolio “bad” rate. This method allows lenders to approve more women without elevating credit risk. The guide notes that lenders should carefully test such approaches and review results regularly to avoid overgeneralisation or bias.

Gender-intentional scorecard development

Lenders can incorporate gender intentionally in model development either by including gender as a characteristic or by building separate models for women and men. The case of AB Bank Zambia illustrates the effectiveness of this approach. When gender was added as one of 14 scorecard characteristics, women’s share of microloans increased from 57% to 60%, while maintaining equal “bad” rates of 3.5% for both genders. TymeBank’s example showed that including gender in its Buy Now Pay Later (BNPL) model increased women’s loan approvals by 6 percentage points while narrowing the gender gap in “bad” rates from 2.1% to 0.9%. Building separate scorecards by gender produced the strongest result, increasing women’s loan approvals by 9 percentage points and expanding total lending by around 4%.

Other gender-intentional strategies

The guide proposes extending gender-lens analysis to risk-based pricing. Where women’s repayment rates are higher, lenders could offer lower interest rates while maintaining profit margins. Using TymeBank data, the model suggested interest rate reductions of 0.5–2% for 90% of women borrowers and up to 18% for higher-risk borrowers. Implementation should consider reputational and regulatory implications, market competition, and selection bias. Risk-based pricing can enhance affordability for women and attract more low-risk borrowers.

Conclusions

Gender-intentional credit scoring enables lenders to measure and price risk more accurately while expanding women’s financial access. Evidence from CGAP’s partner banks shows that even simple gender adjustments can increase total lending and improve portfolio performance. The report recommends ongoing experimentation with digital data and analytics to refine these models and further close the gender credit gap.