This paper outlines key frameworks for reserving validation and techniques employed. Many companies lack an embedded reserve validation framework and validation is viewed as piecemeal and unstructured. The paper outlines a case study demonstrating how successful machine learning techniques will become and then goes on to discuss implications. The paper explores common validation approaches and their role in enhancing governance and confidence. Common weaknesses are covered and components of a reserving validation framework are proposed with the impact of Covid-19 and IFRS 17 examined, before the paper looks at the future and discusses the data challenges to overcome on the path to embedded reserving process validation.