Graph data science is increasingly being used by businesses to combat the growing problem of fraudulent schemes, despite sophisticated fraud detection tools. Traditional models that flag high-risk identities often miss synthetic identity theft and fraud rings, which can result in significant financial losses. Graph data science helps turn this pattern around by augmenting existing analytics and machine learning pipelines, increasing the accuracy and viability of existing fraud detection methods. By analyzing network structures using searches, queries, and graph algorithms, graph data science improves the accuracy of fraud predictions, enabling financial services firms to detect more fraud in their existing data without changing their ML pipeline. This approach also unlocks new insights into patterns indicative of fraud, allowing for real-time operational fraud detection systems to be incorporated and top-line benefits achieved.