Textual Data Mining for Financial Fraud Detection: A Deep Learning
  Approach
Qiuru Li
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.