Digital Inclusive Finance and Family Wealth: Evidence from LightGBM Approach

Abstract

With the rapid development of digital technology in China, Digital Inclusive Finance, which uses digital financial services to promote financial inclusion, is developing rapidly. This paper uses the Peking University Digital Financial Inclusion index of China and China Family Panel Studies (CFPS) data to construct a predictive model using the LightGBM machine learning algorithm to study whether Digital Inclusive Finance can predict household wealth and analyze the characteristics of strong predictive ability for household wealth. They found that: (1) the introduction of the Digital Financial Inclusion index can improve the prediction performance of the household wealth model; (2) financial literacy and age characteristics are the key characteristics of household wealth accumulation; (3) the coverage and depth of Digital Inclusive Finance has a significant effect on family wealth accumulation, but the degree of digitization acts as a disincentive factor. This paper not only uses machine learning methods to do research on Digital Inclusive Finance and family wealth from a more comprehensive perspective, but also provides effective theoretical support for the key factors that enhance family wealth.