Mapping Credit Gender Gaps With Regulatory Data in Rwanda

To assess the true impact of financial services, it’s essential to understand who uses these services, how, and how useful they find them. While granular data enables ideal analysis across customer attributes—like gender, age, location, income, or legal status— in its absence, financial sector authorities can start by analyzing aggregate data segmented by these attributes. This approach provides valuable insights into differences in behaviors, risks, and outcomes across customer groups, revealing gaps in their product application, approval, usage, and termination journey. By analyzing and sharing this segmented data, authorities can develop targeted interventions that promote financial inclusion and protect vulnerable populations, while financial services providers and funders gain actionable information to improve product offerings and allocate resources more effectively.  

To assess the true impact of financial services, it’s essential to understand who uses these services, how, and how useful they find them.

CGAP's ongoing work with regulators illustrates this power. A recent pilot with the National Bank of Rwanda (NBR) tested the use of granular data to analyze segmented indicators that uncovered gaps in financial services access, usage, and outcomes, primarily by gender, but also by age and location. We shared key findings of the pilot at a recent CGAP–NBR workshop in Kigali that convened over 80 representatives of the NBR, other authorities, financial services providers, market facilitators, and funders. The findings sparked valuable discussions between participants and underscored a simple truth: when regulators actively analyze, use, and share disaggregated data, they create a feedback loop that improves decision-making, driving better outcomes for all customers. 

How gender-disaggregated data can enhance credit market analysis

In the credit market, gender-disaggregated data analysis can highlight: 

• Access inequalities: for example, provinces with low shares of women loan officers or agents may also show lower loan approval rates for women borrowers.
• Uptake and usage disparities: comparing borrower numbers and loan values by gender may reveal differences in loan size, borrowing patterns, and debt levels.
• Risk dynamics: analyzing non-performing loan (NPL) ratios, delinquency rates, and collateral seizures over time highlights whether certain groups face higher structural risks.
• Differences in conditions and quality: differences in rejection rates, collateral requirements, and borrowing costs can expose gender biases or unfair treatment. 

These insights allow authorities to stop playing catch-up and instead undertake preemptive oversight that informs policy, regulatory, and supervisory actions in support of women’s needs. Gender-informed interventions – such as adjusted underwriting or collateral requirements, targeted guarantee schemes, and enhanced monitoring of pricing or rejection patterns – can subsequently foster a more accessible, suitable, and affordable financial sector.  

Key gender-informed findings from Rwanda’s credit market

CGAP’s pilot project with the NBR shows gender-disaggregated analysis in action. The NBR maintains a financial inclusion dashboard that tracks access and uptake of financial services in Rwanda, disaggregated by gender, age, and location. The pilot expanded those findings by using data from NBR’s Electronic Data Warehouse to examine not only access and uptake, but also usage patterns, quality, and outcomes. The findings offered concrete evidence of how gender-disaggregated regulatory data can surface important trends across several fronts, including the credit market. Here’s what we found: 

• Women have a lower share of loans: women represent 40% of both active credit account holders and first-time borrowers. While female credit participation is rising overall, the share of young women obtaining credit for the first time has grown only marginally, suggesting an important area for root-cause analysis and a potential opportunity for targeted interventions to remove barriers to credit access.
• Gaps vary by lender type: microfinance institutions (MFIs) serve the highest share of female borrowers (58%), highlighting their pivotal role in women’s financial inclusion. Banks and savings and credit cooperatives (SACCOs) show lower female participation, pointing to opportunities for outreach and product redesign.
• Women receive smaller personal loans but larger productive loans: women make up 49% of personal loan borrowers but hold only 34% of personal loan balances, indicating access to smaller amounts. Yet in productive loans, women hold larger balances and much of the outstanding value, challenging stereotypes about women as conservative or low-risk borrowers.
• Women are better at repayment: women demonstrate strong repayment performance, with fewer loans and loan balances classified as substandard compared to men, and significantly lower NPL ratios.  
• Women face higher rejection rates: despite stronger repayment performance, women’s loan applications are rejected more often, a consistent pattern over the past five years. This suggests potential bias in credit risk assessment or formally gender neutral but de facto stricter requirements for women applicants. 

In nearly all cases, gender gaps are larger for young women. These findings reinforce the need to base financial inclusion strategies and measures on real, disaggregated evidence rather than assumptions. 

Implications for policy and practice

The Rwanda pilot and workshop showed that effective use of disaggregated regulatory data can help: 

• Identify and monitor gaps: By periodically assessing consumer experiences, risks, and outcomes, authorities can better understand where disparities exist and how they evolve.
• Inform policymaking and regulation: Evidence-based insights support actions that address gender gaps across access, usage, quality, and outcomes.
• Guide market action: Funders and financial services providers can use these insights to design customer-centric incentives, interventions, and products that expand fair access to finance.  
• Broaden disaggregation efforts: Encouraging providers and regulators to also analyze data by age, location, disability, and other traits supports richer intersectional analysis and a more inclusive financial system. 

The opportunities are greater than the challenges

The NBR pilot illustrated how regulatory gender-disaggregated data can transform financial inclusion when actively analyzed and shared. It also highlighted common challenges, including data gaps and anomalies, underscoring that data quality improves only when the data is used. Yet, regulators should not wait for “perfect” datasets before beginning analysis. Frequent data use by technical staff creates a feedback loop that exposes quality issues and continuously improves information.  

The NBR pilot illustrated how regulatory gender-disaggregated data can transform financial inclusion when actively analyzed and shared. 

Capacity constraints do present a hurdle, however. Even when disaggregated data is collected, many authorities lack teams with the expertise and time to analyze and interpret data effectively, identify data quality issues, and contribute to data improvements. Building this capacity is an investment that pays off by strengthening not only financial inclusion but also broader mandates such as financial stability, consumer protection, and market development. 

The pilot also underscored the importance of collaboratively engaging with a range of ecosystem actors to highlight the importance of disaggregated data, disseminate and discuss data analytics, and identify key actions to improve regulatory data. Regulators who continually learn from disaggregated data can guide the financial sector to adapt, innovate, and deliver meaningful value, creating a future where every customer across all financial services is truly seen, understood, and served.