Conduction abnormalities (CA) in patients undergoing transcatheter aortic valve implantation (TAVI) has emerged as a growing concern. While the risk of CA arises from a combination of procedural and anatomical variables, precise mechanisms remain unclear. Ultimately, there is a clinical need to identify CA risk markers for improving patient risk stratification. Thus, this study established an in silico platform to investigate CA risk associated with balloon-expandable (BEV) and self-expanding valves (SEV), and tests it for varying implantation depths. Finite element analysis was used to model BEV and SEV deployment into a patient-specific aortic root. BEV deployment involved crimping the frame onto a compliant balloon followed by annular expansion via fluid-cavity interaction; while SEV deployment involved crimping the frame into a sheath, positioning within the aortic root, and then gradually removing the sheath for expansion. For each valve, three implantation depths were investigated: nominal and ±2 mm. Tie constraints were applied to the aortic root, native leaflets, and calcifications, and all contact surfaces were set to hard penalty contact (friction coefficient, 0.26). Material models included hyperelastic (aorta and leaflets), elasto-plastic (BEV, calcifications), super-elastic (SEV) and elastic (balloon, sheath). The maximum principal strain and contact pressure at an anatomical surrogate for CA [1] were measured. Deeper BEV implantation increased the principal strain (>0.25) while SEV strain distributions remained largely insensitive to implantation depth. Interestingly, for the SEV, contact pressure was highest at the nominal position and lowest at +2 mm, likely reflecting the increasing LVOT diameter relative to the aortic root, reducing the frame-tissue contact at deeper positions. In contrast, the BEV demonstrated increasing contact pressure with deeper implantation. This suggests that deeper positioning of shorter-framed devices enhance localised frame-tissue interaction, potentially elevating mechanical loading and procedural risk. Future work will expand this analysis to a larger patient cohort (~30 patients).