The act of masking the origin of illegal funds, to inject them into the economy in seemingly legal manners is called money laundering. Adversaries make use of money laundering to stay undetected when using illegally obtained money, from stealing, fraud, or other criminal activities. These money laundering processes often span multiple institutions or countries. To combat this, anti money laundering systems have evolved, also known as AMLs. AMLs have gone from simple rule-based approaches to using machine learning to analyse money transfer graphs. However, many money laundering operations still go undetected, particularly due to the assumption that all transaction data is centrally accessible. Yet in practice, institutions are not able to share their data with others because of privacy regulations and concerns. This restricts AMLs when deployed in a decentralised setting. This paper presents the first step towards an algorithm that allows two institutions to detect cycles between them, without these institutions exposing their own subgraphs to the other. The method uses a depth first search algorithm to find associated border vertices, then applies data reduction techniques to minimize the data shared between institutions. These border vertices are then compared to infer the presence of a cycle. While not yet deployable in real-world settings, the algorithm demonstrates improved communication and computational complexity over existing solutions and lays the groundwork for future privacy-preserving AML tools.