  Predicting the economy's short-term dynamics -- a vital input to economic
agents' decision-making process -- often uses lagged indicators in linear
models. This is typically sufficient during normal times but could prove
inadequate during crisis periods. This paper aims to demonstrate that
non-traditional and timely data such as retail and wholesale payments, with the
aid of nonlinear machine learning approaches, can provide policymakers with
sophisticated models to accurately estimate key macroeconomic indicators in
near real-time. Moreover, we provide a set of econometric tools to mitigate
overfitting and interpretability challenges in machine learning models to
improve their effectiveness for policy use. Our models with payments data,
nonlinear methods, and tailored cross-validation approaches help improve
macroeconomic nowcasting accuracy up to 40\% -- with higher gains during the
COVID-19 period. We observe that the contribution of payments data for economic
predictions is small and linear during low and normal growth periods. However,
the payments data contribution is large, asymmetrical, and nonlinear during
strong negative or positive growth periods.
