Generating predictions via machine learning

A system, comprising:

• a non-transitory memory;
• and
• one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:
• accessing data pertaining to an incoming list of actors that are currently flagged for engaging in one or more predefined activities;
• accessing historical data pertaining to a plurality of actors that have previously engaged in the one or more predefined activities;
• performing a first machine learning process at least in part by using a regression model trained on the historical data;
• calculating a first score using the trained regression model and the data pertaining to the incoming list of actors;
• performing a second machine learning process at least in part by using a neural network model trained on the historical data;
• calculating a second score using the trained neural network model and the data pertaining to the incoming list of actors;
• calculating a weighted score based on the first score and the second score;
• and
• predicting, based on the weighted score, whether scanning the incoming list of actors against a list of users for matches will cause a number of the matches to exceed a predefined threshold.

A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:

• accessing first data pertaining to a plurality of first entities that have been previously associated with a flagged activity;
• identifying, at least in part by executing a clustering algorithm on the first data, a subset of the first entities that have met a predefined criterion, wherein the subset of the first entities have attributes that are in textual format;
• generating a multi-dimensional matrix having a plurality of vectors, wherein the vectors are obtained by executing a word2vec algorithm using the attributes of the subset of the first entities;
• training a Convolutional Neural Network (CNN) model with the multi-dimensional matrix;
• accessing second data pertaining to an incoming list that contains a plurality of second entities that have been flagged for engaging, or having engaged, in the flagged activity;
• and
• predicting, at least in part based on an output of the trained CNN model, whether scanning the second data against a plurality of third entities for matches will cause a number of alerts to exceed a predefined threshold, wherein each of the alerts corresponds to a match that indicates further investigation, and wherein each of the alerts is associated with a predefined geographical location or with a predefined language.