Iron-rich layers are known to form in the stellar subsurface through a combination of gravitational settling and radiative levitation. Their presence, nature, and detailed structure can affect the excitation process of various stellar pulsation modes and must therefore be modeled carefully in order to better interpret Kepler asteroseismic data. In this paper, we study the interplay between atomic diffusion and fingering convection in A-type stars, as well as its role in the establishment and evolution of iron accumulation layers. To do so, we use a combination of three-dimensional idealized numerical simulations of fingering convection (which neglect radiative transfer and complex opacity effects) and one-dimensional realistic stellar models. Using the three-dimensional simulations, we first validate the mixing prescription for fingering convection recently proposed by Brown et al. (within the scope of the aforementioned approximation) and identify what system parameters (total mass of iron, iron diffusivity, thermal diffusivity, etc.) play a role in the overall evolution of the layer. We then implement the Brown et al. prescription in the Toulouse-Geneva Evolution Code to study the evolution of the iron abundance profile beneath the stellar surface. We find, as first discussed by Théado et al., that when the concurrent settling of helium is ignored, this accumulation rapidly causes an inversion in the mean molecular weight profile, which then drives fingering convection. The latter mixes iron with the surrounding material very efficiently, and the resulting iron layer is very weak. However, taking helium settling into account partially stabilizes the iron profile against fingering convection, and a large iron overabundance can accumulate. The opacity also increases significantly as a result, and in some cases it ultimately triggers dynamical convection. The direct effects of radiative acceleration on the dynamics of fingering convection (especially in the nonlinear regime) remain to be added in the future to improve the quantitative predictions of the model.