Predicting Regional Economic Indices using Big Data of Individual Bank Card Transactions

TL;DR

This study demonstrates that individual bank card transaction data can effectively predict regional economic indices, revealing strong correlations between spending behavior and socioeconomic measures in Spain.

Contribution

It introduces a novel approach using big data from bank transactions combined with machine learning to predict regional economic indicators, expanding analysis beyond traditional official statistics.

Findings

Strong correlation between spending behavior and socioeconomic indexes

Effective prediction of economic indices from transaction data

Potential application to smaller geographic scales

Abstract

For centuries quality of life was a subject of studies across different disciplines. However, only with the emergence of a digital era, it became possible to investigate this topic on a larger scale. Over time it became clear that quality of life not only depends on one, but on three relatively different parameters: social, economic and well-being measures. In this study we focus only on the first two, since the last one is often very subjective and consequently hard to measure. Using a complete set of bank card transactions recorded by Banco Bilbao Vizcaya Argentaria (BBVA) during 2011 in Spain, we first create a feature space by defining various meaningful characteristics of a particular area performance through activity of its businesses, residents and visitors. We then evaluate those quantities by considering available official statistics for Spanish provinces (e.g., housing prices, unemployment rate, life expectancy) and investigate whether they can be predicted based on our feature space. For the purpose of prediction, our study proposes a supervised machine learning approach. Our finding is that there is a clear correlation between individual spending behavior and official socioeconomic indexes denoting quality of life. Moreover, we believe that this modus operandi is useful to understand, predict and analyze the impact of human activity on the wellness of our society on scales for which there is no consistent official statistics available (e.g., cities and towns, districts or smaller neighborhoods).