The stochastic generalised linear bandit is a well-understood model for sequential decision-making problems, with many algorithms achieving near-optimal regret guarantees under immediate feedback. However, the stringent requirement for immediate rewards is unmet in many real-world applications where the reward is almost always delayed. We study the phenomenon of delayed rewards in generalised linear bandits in a theoretical manner. We show that a natural adaptation of an optimistic algorithm to the delayed feedback setting can achieve regret of ̃O(d√T + d3/2E[τ ] ), where E[τ ] denotes the expected delay, d is the dimension and T is the time horizon. This significantly improves upon existing approaches for this setting where the best known regret bound was ̃O(√dT √d + E[τ ] ). We verify our theoretical results through experiments on simulated data.