Assessing incentives to increase digital payment acceptance and usage: A machine learning approach.

An important step to achieve greater financial inclusion is to increase the acceptance and usage of digital payments. Although consumer adoption of digital payments has improved dramatically globally, the acceptance and usage of digital payments for micro, small, and medium-sized retailers (MSMRs) remain challenging. Using random forest estimation, we identify 14 key predictors out of 190 variables with the largest predictive power for MSMR adoption and usage of digital payments.

A machine learning approach to the digitalization of bank customers: Evidence from random and causal forests.

Understanding the digital jump of bank customers is key to design strategies to bring on board and keep online users, as well as to explain the increasing competition from new providers of financial services (such as BigTech and FinTech). This paper employs a machine learning approach to examine the digitalization process of bank customers using a comprehensive consumer finance survey. By employing a set of algorithms (random forests, conditional inference trees and causal forests) this paper identities the features predicting bank customers' digitalization process, illustrates the sequence of consumers' decision-making actions and explores the existence of causal relationships in the digitalization process.

Brain substrates explain differences in the adoption and degree of financial digitalization.

This study analyzes neural responses connected to trust and risk to explain financial digitalization decisions. It shows that brain responses distinctively inform differences in the adoption of digital financial channels that are not shown by any other sociodemographic or behavioral indicators. From a methodological standpoint, the study explores if usage patterns of digital financial channels and instruments are associated with psychological and biological indicators; it uses functional magnetic resonance imaging (fMRI) to investigate whether financial digitalization decisions are linked to the evoked brain response to the safety associated with video images of financial transactions through digitalized and non-digitalized channels; it conducts trust and risk neuro-experiments to identify their impact on financial digitalization decisions and it analyzes whether brain structure is linked to financial digitalization behavior.