Next generation Risk Assessment (NGRA) is an exposure-led, hypothesis-driven approach which integrates new approach methodologies (NAMs) to assure safety without generating animal data. This hypothetical skin allergy risk assessment of two consumer products – face cream containing 0.1% coumarin and deodorant containing 1% coumarin – demonstrates the application of our skin allergy NGRA framework which incorporates our Skin Allergy Risk Assessment (SARA) Model. SARA uses Bayesian statistics to provide a human relevant point of departure and risk metric for a given chemical exposure based upon input data that can include both NAMs and historical in vivo studies. Regardless of whether NAM or in vivo inputs were used, the model predicted that the face cream and deodorant exposures were low and high risk respectively. Using only NAM data resulted in a minor underestimation of risk relative to in vivo. Coumarin is a predicted pro-hapten and consequently, when applying this mechanistic understanding to the selection of NAMs the discordance in relative risk could be minimized. This case study demonstrates how integrating a computational model and generating bespoke NAM data in a weight of evidence framework can build confidence in safety decision making.