Preventing child abuse is a unifying goal. Making decisions that affect the lives of children is an unenviable task assigned to social services in countries around the world. The consequences of incorrectly labelling children as being at risk of abuse or missing signs that children are unsafe are well-documented. Evidence-based decision-making tools are increasingly common in social services provision but few, if any, have used social network data. We analyse a child protection services dataset that includes a network of approximately 5 million social relationships collected by social workers between 1996 and 2016 in New Zealand. We test the potential of information about family networks to improve accuracy of models used to predict the risk of child maltreatment. We simulate integration of the dataset with birth records to construct more complete family network information by including information that would be available earlier if these databases were integrated. Including family network data can improve the performance of models relative to using individual demographic data alone. The best models are those that contain the integrated birth records rather than just the recorded data. Having access to this information at the time a child’s case is first notified to child protection services leads to a particularly marked improvement. Our results quantify the importance of a child’s family network and show that a better understanding of risk can be achieved by linking other commonly available datasets with child protection records to provide the most up-to-date information possible.