This article proposes a new method for estimating claim liabilities. Our approach is based on the observation from contract theory that there is information asymmetry between the insurer and the policyholder about the risks incurred by the latter. We show that unobserved heterogeneity allows for a form of experience learning that can reduce this asymmetry, which makes it easier for the insurer to distinguish between high‐risk and low‐risk claimants. We evaluate our approach in the context of disability insurance for self‐employed and show that it results in more accurate best estimates of outstanding claim liabilities.