Data Collection by Supervisors of Digital Financial Services
21 December 2017
Good data are at the core of effective financial supervision.
Digital financial services (DFS) have grown considerably in emerging markets and developing economies (EMDE), where they are instrumental for financial inclusion. DFS supervision needs to ensure that this expansion happens in a way that facilitates sustained, healthy financial inclusion.
Data are at the core of financial supervision in any country, but supervisors’ data needs and data collection practices vary. Different approaches to data collection can affect data quality and, hence, the effectiveness of supervision. DFS supervisors in EMDE must answer some key questions: Which data should they collect? How frequently? In which format? Through which means? How can they improve data quality? What aspects should be considered when designing data collection mechanisms?
This paper describes DFS data collection practices and the most common reporting requirements with respect to e-money services and issuers and the use of agents and electronic payments. It provides report templates and other sample material that could be useful for DFS supervisors.
DFS reporting requirements (i.e., broad data categories) do not vary substantially across countries, but there is very wide variation in the subcategories of DFS data collected, particularly DFS transaction types. Consequently, there is no single DFS report template that would work for multiple countries. DFS reporting requirements should be based on a mapping of the data needs of each DFS supervisor, and on collaboration and consultation with reporting institutions and authorities that have overlapping data needs.
Weak DFS data collection practices, such as inconsistent use of key DFS concepts, duplicate reporting requirements, and so forth, can lead to poor data quality or higher compliance costs. Moreover, although most countries impose a minimum level of data standardization by using report templates, only a few EMDE supervisors offer comprehensive guidance, such as data dictionaries and taxonomies, to reporting institutions. Quality data and comparative supervisory analyses rely on standardization.
The fast growth of RegTech and SupTech creates opportunities for EMDE supervisors to improve their DFS data collection and supervisory approaches. One issue covered in this paper is whether data should be granular or aggregated. There are benefits and challenges in collecting and using granular data, but the central question is whether the data collection mechanism is adequate to handle granular data without jeopardizing data quality and increasing compliance costs. DFS supervisors need to consider revamping their data collection mechanisms, to reduce reliance on templates and fully automate reporting and collection procedures.
Finally, while high-quality data are necessary for effective DFS supervision, they are not enough. Without good analytical skills and sufficient capacity to analyze data and produce accurate and timely supervisory intelligence, reforms to data collection will have limited impact.