Using Gender-Disaggregated Data

A primary issue is that many financial service providers do not currently collect these data fields in a consistent or structured way, meaning institutions would need to redesign onboarding processes, update core systems, and address gaps or inaccuracies in historical data. Even where data are collected, customers may choose not to disclose certain attributes, leading to completeness and reliability concerns that can affect the usefulness of the reporting.

There are also important legal and privacy considerations. Collecting sensitive demographic information requires clear consent frameworks, strong safeguards, and clear articulation of purpose to ensure compliance with data protection obligations and to maintain customer trust. Without careful communication, customers may perceive the data collection as intrusive, which could discourage engagement with formal financial services.

Another challenge lies in standardization. Regulators would need to define consistent taxonomies and reporting definitions—such as how gender categories are captured, how income or vulnerability segments are classified, and the level of geographic detail required. Without uniform standards, comparisons across institutions would be unreliable and supervisory insights could be distorted.

Technology and cost constraints are also significant, particularly for smaller providers. Legacy systems may not support additional data fields or granular reporting, requiring investment in system upgrades, data architecture, and reporting tools. This increases operational burden, including more complex validation, reconciliation, and data governance requirements, and may expose skill gaps in data management and analytics capabilities.

Finally, there is a risk that highly granular demographic data could be misinterpreted if not analyzed in context, potentially leading to flawed policy conclusions or reputational sensitivities for institutions. Securing industry buy-in therefore requires a proportional approach that balances supervisory value with implementation feasibility, clear guidance, and phased timelines to ensure the framework enhances inclusion insights without imposing undue burden.

Smaller Financial Services Providers may lack the infrastructure to generate disaggregated reports which may result in either non-compliance or the production of low-quality data that frustrates the overall goal.
There is a concern for customer consent and privacy where customers may be reluctant to disclose personal information beyond what is strictly necessary to access a service.

The key challenges include poor availability and quality of disaggregated customer data, limited IT systems and technical capacity among FSPs, lack of standardized reporting frameworks, skills gaps, and possible resistance from providers especially digital and mobile money operators without clear guidance and strong data protection assurances.

I think we would face a challenge around the organisational culture. This is because supervisors would need to actively integrate demographic insights into prudential supervision and without internal buy-in and training, the additional data may be collected but not meaningfully used.

Another important challenge is integrating the new granular, gender-disaggregated reporting requirements into existing regulatory reporting frameworks without creating duplication or inconsistencies. Regulators often already have multiple reporting templates for prudential, market conduct, and consumer protection purposes, and adding demographic fields may require redesigning data taxonomies, reporting frequencies, and submission channels. If integration is not well coordinated, financial service providers may be forced to submit similar information through parallel systems, increasing compliance complexity and the risk of conflicting data. Additionally, aligning new demographic indicators with legacy datasets can be technically difficult, particularly where historical data was not originally collected in a disaggregated format, limiting trend analysis and comparability over time.

Implementing a highly granular, gender disintergrated regulatory reporting system would present several challanges, first data availability and quality may be limited especiallay if FSPs do not currently collect consistent or reliable demographic data. Second, there could be concerns aroudn data privacy and consent and then FSPs may face higher compliance costs and system upgrades to capture and report data in more detailed manner. Balancing detailed reporting with propotionality and data protection would be essential.

Implementing such a system would likely face significant data privacy and cybersecurity challenges, as collecting and storing highly granular, identifiable demographic data increases the risk and impact of potential data breaches. Supervisors would also struggle with data quality and industry resistance, as many legacy financial service providers (FSPs) currently lack the back-end systems required to capture and report disaggregated customer information accurately. Finally, the "data explosion" from such granular reporting could lead to a "data swamp" if the regulator lacks the advanced infrastructure and specialized analytical skills needed to process and interpret millions of individual data points effectively.

A highly granular reporting system disaggregated by gender and other demographics would face several challenges, many FSPs lack the systems or capacity to collect detailed data consistently, definitions of demographic categories may vary across institutions, and privacy concerns could make customers reluctant to share sensitive information. Smaller providers could struggle with the added compliance burden, while supervisors themselves would need new skills and tools to analyze large, complex datasets. Without careful phasing and clear standards, the effort could overwhelm both FSPs and regulators instead of improving inclusion.

Implementing a granular reporting system that breaks down data by gender and other demographics is no easy feat. FSPs face difficulties in gathering uniform data because different demographic definitions create multiple ways to interpret demographic information. Customers would refuse to share their private information because they fear their personal data would be misused. Smaller providers will experience difficulties with compliance obligations because regulatory bodies require more advanced data analysis skills to interpret the information. The system will create more confusion than understanding if its implementation does not occur through careful planning.

One key challenge would be data quality and consistency, especially if smaller FSPs rely on manual processes or have limited system capabilities to capture detailed demographic information. There could also be privacy and legal concerns around collecting and sharing sensitive customer data.

Another challenge would be internal capacity. Supervisors would need stronger data governance and analytical skills to actually use granular gender and demographic data effectively, rather than just collecting it.

Implementing a highly granular, gender-disaggregated regulatory reporting system would be challenged by weak data quality across FSPs, inconsistencies in demographic capture, legacy system limitations, increased compliance costs and heightened data-privacy and cybersecurity risks that could undermine supervisory reliability

In context of Pakistan, even though gender information is formally captured at the time of establishing customer relationships, a key practical challenge lies in the readiness of FSPs’ systems to accurately extract and report this data in a consistent and automated manner. In many cases, gender is recorded at onboarding but not embedded as a structured, reportable data field across all product modules. This makes reporting complex and prone to manual intervention. SBP’s Banking on Equality Policy has institutionalized the requirement to record and report gender-disaggregated data, however the systems integration and data architecture across banks and DFIs is still not consistent which can affect data accuracy and comparability.

One of the main challenges is the existing legacy systems that cannot easily be changed to accomodate new data requirements. This would mean FSPs need an additional intervention to include aspects of disaggregation. Another would be consistency in reporting/classification across the whole sector.

The primary issues that I would face has to do with the infrastructure sophistication of the reporting institution. For example, currently some institutions in Nigeria have access to the National Identity database to spool some data (which include gender, age and other demographics) using API, while some are not yet sophisticated for this. for that not sophisticated, they report manually which is usually prone to error, and a wide gap from those that spool and validate without human intervention.

The second is the issue of social desirability, and backlash on policy change to include other gender aside male or female in customer forms or data page

The FSP current systems or core banking systems do not have data fields collecting gender-disaggregated data and creating a challenge of upgrading the systems, reporting standards returns and templates used to collect granular data.
The FSP face a challenge of collecting additional information on gender which would need customer's consent to this personal data and would have issues around data privacy laws.
The challenge of the FSP would be the updating or revising the laws, regulations, policies and manuals to include the granular data that would be included or required to be included in the statutory returns template and also improve the historical data that is being updated.

FSP Collection Burden: Providers must update systems, forms to capture gender plus demographics (age, income, location, etc.)
Access & Integration Issues: Granular methods, require secure access to external databases; fragmentation, permissions, and error risks craete complication.
Data Quality & Consistency: Multiple breakdowns increase error risks; source conflicts and lack of standardization across providers reduce reliability.
Infrastructure & Skills Gaps: Current IT lacks scalability and Sup Tech readiness, emerging markets face budget constraints and shortages of data analysts.
Governance & Risk Heightened: Sensitive data amplifies privacy, security, bias, and compliance risks.
Provider Resistance & Coordination: Compliance costs and complexity lead to pushback, coordinating diverse FSPs (banks, fintech's, mobile operators) requires mandates, pilots, and support.

It may be difficult to obtain complete data which may make it difficult when it comes to accessing the data availed. Standardisation of the data may also become an issue to deal with.

Where data is manually collected or semi-automated, collecting more granular data increase the challenge to analysis and deriving insights. But where data is collected in fully automated system/platform, then it makes it much more easier to increase the data fields and collect more insights. The only challenge here is that smaller operators would complain on the cost of system upgrade. Proportionality principle could be applied to tame this challenge.