Abstract
This literature review examines the relationship between the number and type of deficiencies identified during Port State Control (PSC) inspections and a ship’s overall risk. The main objective is to synthesise the current academic evidence, detailing the analytical methodologies employed and highlighting key research contributions. The selection of literature has focused on peer-reviewed articles and relevant doctoral theses addressing detention risk prediction, accident risk and ship risk profiling. The findings indicate a consistent correlation between PSC deficiencies and ship risk, although the nature and strength of this correlation may vary depending on the type of risk considered and the specific deficiencies. A methodological evolution is observed in the field, from descriptive statistical analyses and regressions towards more complex predictive models, such as Machine Learning (ML) and Bayesian Networks (BNs). This transition reflects a search for greater accuracy in risk assessment, going beyond simple numerical correlation to improve the selection of ships for inspection. Multivariate statistical techniques, on the other hand, focus on the identification of risk patterns and the evaluation of the PSC system. The conclusions underline the importance of deficiencies as indicators of risk, the need for differentiated inspection approaches and the persistent challenges related to data quality and model interpretability.