Big Data: Ideal for Financial Reporting and for Tracking Covid-19 Impact
Spanish bank BBVA has used big data to examine the impact of Covid-19 on Spanish consumption. The conclusion is notable: BBVA found a 49 percent average decline in consumer spending.
The bank’s study “Tracking the Corona Crisis with High-Resolution Transaction Data” looked at anonymized and aggregated data related to 1.4 billion cards or BBVA point-of-sale transactions since 2019, which demonstrated the changes in Spanish consumption habits and the exceptional impact the crisis has had on spending.
The data revealed a hoarding trend just before the declaration of the state of emergency, with a nearly 20 percent increase in card transactions. After the state of emergency was declared, consumption in Spain fell dramatically. The average per person daily spending dropped 49 percent compared with the same day a year ago.
The data also showed an impact on different sectors. Food was the only sector that registered an increase in spending. Spending in the other sectors (nonessential consumer goods and services) dropped by more than 90 percent.
Credit and debit card transaction data also allowed BBVA to analyze the impact by region. The crisis appears to have hit Madrid the hardest with a drop in credit card spending of 70 percent.
By revealing costs and how they are being distributed across the Spanish economy, the authors believe that analysis using big data techniques can quantify the impact of the crisis and demonstrate the steps that should be taken to contain the pandemic. The article argues that this type of exercise can be replicated and applied to other contexts. The researchers conclude by calling for public-private and academic collaboration to further explore the application of big data techniques on economic and financial analysis.
Policymakers also may benefit from working with such high-volume, highly granular data. XBRL International argues that using the information of this sort raises various questions about security and data privacy as well as moral hazard. It also argues, however, that interest is increasing in granular data from financial regulators worldwide. At the same time, the use of structured data with XBRL taxonomies within very large data collections is increasing as well. Combined, these new developments mean that XBRL standards need to provide new ways to exchange information in highly efficient ways. xBRL-CSV combines the exceptionally efficient CSV format, with the XBRL benefits of taxonomy-backed structured data. For regulators to efficiently handle extremely large data sets, such efforts will be made easier with the imminent release of xBRL-CSV.
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