This research examines RAFA-BioAuth, a risk-adaptive, fairness-aware framework for mobile banking in cases of presentations and facial occlusions. The proposed solution combines aspects of: Identity Similarity, Passive Presentation Attack Detection (PAD), Asymmetric Financial-Risk Estimation and Fairness Regularization at the Identity Level. For evaluation purposes, all benchmark datasets were split into subject disjoint training, validation, and testing sets. The threshold values from the validation set were used. Bootstrap resampling was employed to estimate the variance. Monte Carlo simulations were performed to estimate the risk. The results showed that identity verification (AUC = 0.548) and PAD (AUC ≈ 0.55) were poor. Comparing RAFA to the AND rule resulted in FAR = 0.20, FRR = 0.31, and expected risk = 340. On the other hand, the AND rule had lower FAR of 0.09, but increased FRR to 0.78. Finally, a conservative end-to-end approach produced an FRR of 0.686. Thus, our results indicate trade-offs rather than production-readiness as we did not perform deployment, cross-device, or cross-dataset validations on our solutions. We present the contributions of this research as being an interpretable integration and not a new algorithm.