The estimation of a multivariate mean $θ$ is considered under natural modifications of balanced loss function of the form: (i) $ω\, ρ(\|δ-δ_0\|^2) + (1-ω) \, ρ(\|δ-θ\|^2) $, and (ii) $\ell \left( ω\, \|δ-δ_0\|^2 + (1-ω) \, \|δ-θ\|^2 \right)\,$, where $δ_0$ is a target estimator of $γ(θ)$. After briefly reviewing known results for original balanced loss with identity $ρ$ or $\ell$, we provide, for increasing and concave $ρ$ and $\ell$ which also satisfy a completely monotone property, Baranchik-type estimators of $θ$ which dominate the benchmark $δ_0(X)=X$ for $X$ either distributed as multivariate normal or as a scale mixture of normals. Implications are given with respect to model robustness and simultaneous dominance with respect to either $ρ$ or $\ell