A plurality of first entities have been previously associated with a predefined activity. By performing a clustering algorithm on the first entities, a subset of the first entities is identified that have met a predefined criterion. Via a Natural Language Processing (NLP) technique, a multi-dimensional matrix is generated. The matrix has a plurality of vectors associated with attributes of the subset of the first entities. A neural network model is trained with the multi-dimensional matrix. A plurality of second entities are on a list that contains entities that have been flagged for engaging in, or having engaged, the predefined activity. Based on the trained neural network model, a prediction is made whether scanning the second entities against a plurality of third entities for matches will cause a number of alerts having a predefined characteristic to exceed a predefined threshold. The alerts correspond to matches that needs further investigation.