Most anomaly detection models are developed by using expert system methods that mimic human experts. The process to capture the expertise honed by fraud examiners is complicated and practically challenging, often resulting in suboptimal models. This study proposes a clustering-based model that captures hidden characteristics of potentially fraudulent wire transfers with less human intervention and expertise. Clustering methods classify and group observations with similar characteristics, excluding anomalies from major clusters. The choice of a clustering method and its parameters is often subjective and significantly affects a set of resulting clusters. In order to reduce the subjectivity of a clustering method while retaining its strength, this study proposes a clustering model with Density Based Spatial Clustering of Applications with Noise (DBSCAN) to detect potentially fraudulent wire transfers of an insurance company. The results show that the DBSCAN models identifies hidden relationships between the variables not only included but also excluded for the modeling with noise wire transfers while less human intervention is needed for clustering parameter selections.