Shell companies are often used for illicit activities such as money laundering, sanction evasion, bribery, and financing terrorism due to their ease of incorporation, disposability, and anonymity. Researchers have developed a model using graph analytics in Neo4j to detect these entities, which can be identified by analyzing those already accused or identified in money laundering cases. The UK was chosen for its public register of ownership for companies, which revealed a dichotomy between theoretical standards and observable activity, providing an interesting case study. A dataset of 800 shell companies was used, with information collected from various sources including corporate databases and government registries. Graph database and graph data science were used to analyze the data, revealing valuable insights such as individual executives with multiple appointments in numerous entities, and a significant gap between theoretical standards and observable activity. The research also explored the use of graph algorithms and supervised learning to improve performance metrics, setting a base for future research work.