Fintech and statistics

Since the beginning of the last decade, they are with us. Fintech companies are innovative and technology oriented and they provide financial services: either old services in new ways or altogether new services.

For central bank statistics, fintech companies are a challenge. What they do is relevant, no doubt, but typically there is no reporting obligation, quite often there is not even a fitting statistical description of their activity. The system of industrial classification is the backbone of statistical reporting, but it is not able to distinguish innovative financial activity from traditional finance. You may find the problem characterized here.

Fintech is a global phenomenon, and the Irving Fisher Committee on Central Bank Statistics (IFC) of the Bank for International Settlements (BIS) has organized a Working Group on Fintech data issues to better understand the nature of this statistical challenge and to make suggestions for improvements. I was involved in this work, which is forward-looking and pioneering in many respects. In August 2019, a seminar in Kuala Lumpur collected first results. In July 2020, the final report of the Working Group was published by the BIS, laying out a roadmap towards setting up statistical Fintech monitoring systems.

Surveys are a classical statistical instrument, which can be especially useful in situations where the field to be described is open and subject to change. In collaboration with colleges from the Research Centre of the Deutsche Bundesbank, I helped to design a survey on fintech use by German households, as a module of the Panel of Household Finance (PHF), see below. Here are first results of this fascinating study.

IMIDIAS – A hub for micro data at the Deutsche Bundesbank

In 2013, the Statistics Department of the Deutsche Bundesbank was mandated:to establish an integrated interdepartmental information system for analytical and research purposes; to define governance and roles; to develop a Research Data and Service Centre (RDSC), to develop a statistical microdata warehouse; and to step up active support for research projects.

The “integrated microdata-based information and analysis system” (IMIDIAS) initiative aimed at a coherent solution to these requirements. IMIDIAS is a pragmatic approach that leaves the core production system untouched and keeps data management decentralised. 

Briefly, IMIDIAS is meant to perform data integration ex post, using the finalised and quality-controlled process data as a sources layer for a statistical data warehouse, the House of Micro Data (HoM). In the HoM, data are integrated on the basis of joint reference data and made accessible by means of an SDMX superstructure. On this basis, a newly created Research Data and Service Centre (RDSC) offers data and analysis services, for analysts and researchers. The ultimate aim of IMIDIAS is to make available what is already there, in a consistent, effective, cost-efficient and user-friendly way, for designated purposes and complying with strict confidentiality rules. 

More information: 

Irving Fisher Committee on Central Bank Statistics, Data-sharing: issues and good practices, Report to BIS Governors prepared by the Task Force on Data Sharing, January 2015, pp 39.

Ulf von Kalckreuth, A Research Data and Service Centre (RDSC) at the Deutsche Bundesbank a draft concept. IFC-Bulletin No 37, Irving-Fisher Committee on Central Bank Statistics, 2014.

The PHF – a survey on household finances and wealth in Germany

The Panel on Household Finances (PHF) is an encompassing panel survey on household finances and wealth in Germany conducted by the Deutsche Bundesbank. It covers the balance sheets, pension claims, savings, incomes and work histories of households, together with some information on consumption patterns, attitudes, expectations and standard demographic characteristics. 

A representative sample comprising 3,565 households provide data for the first survey wave between September 2010 and July 2011. Wealthy households were oversampled on the basis of micro-geographic indicators in order to shed light on the distribution and the composition of wealth across households. 

The PHF data data provide a comprehensive view of households’ assets and debts and their determinants, thus allowing a better understanding of issues such as saving and consumption behaviour, the distribution of wealth or insolvency risks. The anonymised micro data are available for scientific use. Because the PHF is part of the HFCS, a harmonised survey coordinated by the ECB being carried out in all euro-area countries, it is relatively easy to place the German results in a European context. The high data quality makes it a fruitful resource for researchers and monetary policymakers alike.

The results of the first wave of the PHF and the HFCS at large were published in March 2013, revealing a surprising degree of heterogeneity within Germany and in Europe. The data for the second wave were collected in 2014. As many households as possible from the first wave were recontacted, thereby creating a full panel. In 2020, the PHF is preparing the fourth wave.

More information:

PHF survey website