The FinNeeds approach can be applied in a variety of ways, depending on the context and the needs of the policymaker or implementing agency. It can be implemented using both demand and supply-side approaches. Demand-side approaches include implementing a standalone FinNeeds survey or integrating a FinNeeds module or questions into an existing survey. FinNeeds can also be used as an analytical framework to derive insights from existing financial inclusion datasets.
On the supply-side, the FinNeeds framework can be applied to analyse financial service provider data on financial service transactions or customer engagement. This is known as transactional data .
Demand-side data and transactional data can also be merged to give a complete picture of individuals’ financial lives – of actual transaction patterns in formal financial services, as well as of self-reported uptake and usage of a broader set of financial devices.
Below, we outline what each methodology entails and give some considerations on when it’s appropriate to apply.
If a country does not run a financial inclusion demand-side survey, or an existing demand-side survey is not sufficient to populate the FinNeeds framework, then a standalone survey is the best way to implement FinNeeds measurement. Typically, a FinNeeds questionnaire would include a module on each of the four financial needs. Each need module incorporates the incidence of use cases in that need category, the devices used towards each use case and, where relevant, outcomes related to that need. This is supplemented by modules on general demographics, usage patterns for different device categories, as well as drivers of use.
When is it appropriate? The main benefit of implementing a standalone survey is that it is tailored to the FinNeeds approach, therefore providing insights on the full FinNeeds framework. However, a standalone survey requires dedicated resources. In countries where a nationally representative financial inclusion survey is already rolled out, resource constraints may mean that there is not also scope for a full nationally representative FinNeeds survey. In such instances, a more limited sample approach can be considered in order to render indicative or regionally representative findings. Alternatively, a version of FinNeeds measurement can be incorporated into an existing survey.
Where a financial inclusion survey is already in place and there are not enough resources to implement a full FinNeeds survey, the framework can be incorporated in existing surveys. This can be done either via the inclusion of a separate module, or by adjusting the existing survey questions. In such cases, it may be necessary to choose one need for which use cases can be comprehensively tested, or to zoom in on select priority use cases, as there is unlikely to be scope to cover the full spectrum of questions included in a standalone survey. As a minimum, it should be possible to identify devices used per use case either from existing questions or through the addition of new questions.
When is it appropriate? Integrating the FinNeeds framework in an existing survey is less resource-intensive than rolling out a dedicated, parallel survey. “Piggy-backing” on an existing survey that is rolled out according to a regular schedule also ensures sustainability and the build-up of time-series data on FinNeeds indicators over time. However, adding FinNeeds questions may increase the time required to administer the survey, thereby impacting on data quality. Implementing agencies may also be reluctant to change the structure of a financial inclusion survey that has been providing a certain set of indicators over time.
FinNeeds can be used as an analytical framework to draw insights from existing demand-side data , even in the absence of a dedicated FinNeeds survey or survey adaptation. This involves inferring use cases and needs from existing survey questions. For example, financial inclusion surveys often ask people to specify the reason for using a particular type of financial service. The answers may be used to infer different use cases underlying uptake of different types of devices. This allows the analyst to gauge the extent to which different types of financial devices are used towards specific use cases or needs. For more detail on the analytical framework and methodology for analysing existing data according to the FinNeeds lens, see section 5. Analyse.
When is it appropriate? Using existing data is attractive for countries that have already institutionalised a financial inclusion survey and do not have scope to change the structure of the survey. However, retrofitting the framework to a dataset not designed around FinNeeds will mean you will not be able to render comprehensive insights across the full FinNeeds framework. For example, you may only be able to pronounce on some use cases and devices, not others, and may not have full usage indicators.
Financial service provider data, or transactional data, refers to any dataset on customer engagements collected by a financial service provider. The nature of the data will differ depending on the type of financial institution and the type of account(s) or financial product(s) included.
The FinNeeds approach can only draw on datasets with individual-level transactional data. Individual usage profiles can then be populated using unique identifiers. Data sources that can be used in populating FinNeeds include:
Financial service providers: Financial service providers such as banks, mobile network operators and microfinance institutions are potential sources of transactional data.
Transactional data aggregators: Depending on the country context, aggregated transactional data can be sourced from a number of data aggregators, including:
When is it appropriate? Transactional data is an objective data source that does not rely on consumer recollection. Nor does it suffer from human error or bias. Hence it can provide a more accurate and granular picture of usage than demand-side data sources. It is also possible to analyse different channels and merchant types to derive insights on use cases.
The main downside of such data, however, is that transactional data only shows one aspect of an individual’s financial life and, hence, may lead to inaccurate conclusions if viewed in isolation. For example, while it can tell you how an individual with a bank account transacts on that account, it cannot pronounce on what other financial services that individual has or how their usage of the bank account is explained in the context of their other financial devices. Moreover, for financial inclusion, where many individuals do not have any form of formal financial account, financial service provider data often does not cover a substantial part of the population. Demand-side data may thus be the only way to ensure that individuals from all backgrounds are included in the research.
Another important constraint is that transactional data is proprietary. It is therefore necessary to obtain buy-in from a financial service provider to share an extract of their data – a process that can be time-consuming and will be subject to a non-disclosure agreement.
Given the strengths and limitations of demand-side and transactional data, a combination of demand and transactional data has scope to render the most granular FinNeeds insights. This is achieved by (1) rolling out a demand-side survey, be it a standalone FinNeeds questionnaire or integrated in an existing survey; (2) obtaining transactional data on usage patterns; and (3) administering the FinNeeds survey to a number of targeted respondents drawn from the financial service provider sample database, to create a merged or linked dataset that connects the demand and supply-side transactional data together.
A merged dataset will show detailed usage patterns for financial service provider customers, as well as give a window into the broader financial life, use case and devices of those respondents outside of that particular provider’s products. A merged dataset can provide unique insight into trends in formal financial service usage in the context of the underlying financial needs, the usage drivers and the broader device portfolio of formal sector users that policymakers, regulators and financial service providers do not normally have sight of.
When is it appropriate? A major limitation to merged data is that it is resource intensive to obtain, as it requires a survey to be rolled out, plus negotiations with one or more financial service provider to source their data. One of the biggest challenges encountered in the insight2impact pilot studies was the roll-out of the demand-side survey to respondents in the transactional supply-side database. This required the financial service provider to share a contact list of a sample of clients with the research house conducting the demand-side survey (which is subject to confidentiality constraints), and for the research house to successfully recruit respondents from a targeted list of clients residing in different areas. Thus, the normal random sampling approach cannot be applied. It is therefore only a viable data source if there is a willing financial service provider partner and sufficient resources and time to achieve meaningful results.
Learn more. For an overview of the analytical framework and approach to creating and analysing a merged dataset, see section 5. Analyse.
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