Digitalization holds promises to unlock savings accumulation in Africa. This paper predicts the mobile savings, taking advantage of machine learning to examine a massive set of features known theoretically and empirically to affect household savings behavior. The algorithm fits a generalized regression model through a penalized maximum likelihood. The work anchors in economic theory on savings but differs from the method standpoint to elicit mobile banking savings take-up and savings accumulation. Data covered thirty-six villages in Western Kenya. Predictors clustered into livelihoods, assets inventory, formation and use of assets, income generation and its use, food composition, intake and nutrition, the housing quality and tenureship, water and sanitation, energy use, family structure, social status architecture, adverse shocks to agricultural production (crop and livestock loss events) and demographics. Data include 1600 households from six different communities and 7,700 quarterly observations from 2013 to 2015. Although the results point to shreds of evidence that corroborate previous findings, we highlight in this paper mobile banking's role in the savings accounts ownership and savings accumulation, as well as the effects of age and employment status. Mobile phone ownership is a strong and significant predictor of savings account ownership. However, the mobile phone is a vehicle to fluctuating savings balance and encourages dissaving. Moreover, being young and in the workforce increases the mobile banking savings take-up but also the dissaving behavior, compared to older age group. This work subsets features that could improve product or policy design towards a better financial inclusion of rural poor.