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

TL;DR

This paper explores deep learning models for NLP-based financial fraud detection, comparing various neural networks to improve accuracy in identifying fraudulent companies from regulatory texts.

Contribution

It introduces a comprehensive comparison of neural network models for NLP-based financial fraud detection, highlighting their effectiveness in analyzing regulatory and financial reports.

Findings

LSTM and GRU outperform other models in accuracy

Deep learning models significantly improve fraud detection capabilities

The approach offers valuable insights for industry and regulators

Abstract

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.