OBJECTIVES: To develop a financial risk forecasting model that could be used to negotiate risk-sharing schemes (RSS) conditions. RSS are increasingly being established as a market access strategy and have direct financial implications for both payer and manufacturer. However, underlying methodologies remain poorly researched. Additionally, HTA agencies may need analytical frameworks to evaluate the value of RSS. METHODS: We designed a financial-based agreement for a hypothetical technology. The financial risk to be shared is defined as N×p×r where N is the size of the target patients population, p is the price/dose of the technology, r is the proportion of patients for whom di>D, di being the number of doses/year given to an individual patient i and D being the maximum number of doses/year agreed in the schemes (the cap). A Logistic growth curve is used to simulate the risk evolution as time progresses and patients progressively accrue in the RSS. Multiple risk evolution and “sharing” scenarios with their resulting financial implications for both parties are simulated. Finally, a Bayesian framework is introduced to allow both parties to make revisions as real-life information becomes available upon implementation of the RSS. RESULTS: For N=1000 over a period of 3 year, D=12 and a prior distribution for di centred on 12 doses but with 20% of patients receiving more than 12 doses, the model predicts that 23,092 doses will be delivered and that 542 doses (2.40%) will fall above the cap. These doses in excess determine the cost to be shared: total refund, partial refund or price discount. CONCLUSIONS: Financial modelling and technological forecasting techniques can be combined to simulate different risk-sharing scenarios and their financial implications for payers and manufacturers. This provides both parties with an analytical framework to design win-win schemes and to make potential revisions as real-life information becomes available.