Textual Data Mining for Financial Fraud Detection: A Deep Learning Approach
This work addresses financial fraud detection for industry practitioners, regulators, and researchers, but it is incremental as it applies existing methods to a specific domain.
The paper tackles financial fraud detection by applying deep learning models like MLP, RNN, LSTM, and GRU to classify textual data from MD&A reports, aiming to compare their accuracy in identifying fraudulent companies.
In this report, I present a deep learning approach to conduct a natural language processing (hereafter NLP) binary classification task for analyzing financial-fraud texts. First, I searched for regulatory announcements and enforcement bulletins from HKEX news to define fraudulent companies and to extract their MD&A reports before I organized the sentences from the reports with labels and reporting time. My methodology involved different kinds of neural network models, including Multilayer Perceptrons with Embedding layers, vanilla Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) for the text classification task. By utilizing this diverse set of models, I aim to perform a comprehensive comparison of their accuracy in detecting financial fraud. My results bring significant implications for financial fraud detection as this work contributes to the growing body of research at the intersection of deep learning, NLP, and finance, providing valuable insights for industry practitioners, regulators, and researchers in the pursuit of more robust and effective fraud detection methodologies.