Automation of fraud detection with machine learning utilizing publicly available forms

Abstract

Systems and methods for alerting an organization about activity that may be fraudulent. Systems may include a computer processor, a storage module, a cleaning module, a preprocessing module, a features extraction module, and a machine learning module. The computer processor may be configured to run a fraud detection engine by collecting publicly available electronic forms every 36 hours, using the modules to store the forms, clean the data, preprocess the data, and run a machine learning model to extract features and to determine if a threshold indicating a risk of fraud has been exceeded. The machine learning models include a liquid, solvency, and profitability ratio classification model, a disclosure classification model, a sentiment analysis model, an anomaly detection classification model, an ownership analysis classification model, and an ESG disclosure classification model. When exceeding a threshold, the computer processor may notify an administrator of the exceeded threshold's identity.