México | Patrones de consumo de efectivo vs. tarjeta: una aproximación Big Data

Summary

Se propone una metodología que combina datos de operaciones con tarjeta de BBVA y de efectivo en supermercados de Frogtek a través de Clarity AI. Se estudian los cambios en patrones de consumo en relación con las variaciones de ingresos, incluyendo la evolución del consumo de bienes y el uso de distintos canales de pago.

Key points

• Key points:
• The analysis of household consumption patterns is relevant for social welfare, policy design, and economic analysis. Recent advances in Big Data and data science techniques allow us to measure consumption trends in an extraordinarily granular way
• Traditional empirical analyses of consumer behavior based on household consumer surveys provide an incomplete picture, which is why in this analysis a new method is used
• BBVA and Clarity AI have joined forces to contribute knowledge to society by showing the analysis of how individuals allocate their card and cash purchases using a variety of econometric and Machine Learning Models.
• Shapley values are used to gain explainability in the Machine Learning models applied, finding Random Forest to be the champion model, which achieves R2 scores above 0.92.
• The results show that the most relevant variables to increase the expenditure by card relative to cash are the changes in income, living in an urban center, and the financial deepening effects.

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