Page 152 - Special Topic Session (STS) - Volume 1

STS422 Damola Owalade
o Accuracy
In some cases, address and contact details are not available for customers.
There is no data on gender and age categories limiting the ability to generate
disaggregated analyses. There could be room for improvement in the data
templates (for onboarding customers) used by microfinance banks and
microfinance institutions, and the data sharing arrangement between the
credit providers and the credit bureau.
 Non-sampling errors
o Securing interviews to merge admin with survey data
Out of the 700 (which was subsequently increased) customer details
provider, only 207 participated in the survey. This meant that intended sample
cluster at a province level was not tenable. To improve the response rates
would have required involving all the credit providers (which the credit bureau
had data for) to reach out to their customers, informing them of the research.
In this case, the credit providers did not participate in the project. A call centre
was initially set up by the credit bureau to secure interviews which led to low
levels of confirmations. However, the research house was more successful in
securing interviews with respondents.
Other challenges in recruiting respondents with administrative data
included unavailability of respondents (due to cold calls) while others were not
willing to participate in the study. This resulted in high substitution rates.
Nigeria
8
The insight2impact collaborated with the Nigeria Interbank Settlement
9
System (NIBSS) to analyse data generated through card and mobile phone-
based transactions for interbank transaction. The aim of the collaboration is to
understand and provide some stylised facts on user characteristics of
payments made using digital financial platforms like banking apps and USSD .
10
The types of transactions include those conducted using include mobile
phone, internet banking, use of cards through point-of-sale devices, use of
bank branches and merchant payments. The data pulled considered
transaction histories over a period of 18 months (June 2016 to December
2018), depending on the platforms under consideration and the analysis being
conducted. Aside from transactions data, the data included some
demographic data for each BVN, including age, gender and location.

8 The research project was conducted between July and December 2018. The project team
consisted of the i2i, 71point4 and AC Nielsen Nigeria that conducted the fieldwork
9 Data provided by NIBSS included NIBSS Instant Payment (NIP), Point of Sale transactions
(POS), Cheque transactions, NIBSS Electronic Funds Transfer (NEFT), CMMS transactions and
mCASH

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