Enron corpus fraud detection

Abstract

The main motive behind this work is to identify the person of interest based on the email data from the Enron corpus which is made public for research. Fraud detection is done using artificial neural network (ANN) with Adam optimizer and ReLU activation functions which is a machine learning approach. With advancements in the field of Artificial Intelligence the fraud detection can done effectively in python environment. This work achieves greater accuracy in terms of recall, precision and F1 score. The work can prove useful to various firms that maintain accounting data of the financial transactions that take place in the given organization. The goal is to devise a method that can be implemented on accounting data of an organization, company or firm to identify the individuals susceptible of committing fraudulent activities by manipulating the financial statements to mislead the investors and shareholders. This ultimately aims to reduce the losses suffered by the investors and shareholders by detection of various fraudulent entities in the given organization.