The nature of statistical learning theory
The nature of statistical learning theory
Self organizing neural networks for financial diagnosis
Decision Support Systems
Automatic text representation, classification and labeling in European law
Proceedings of the 8th international conference on Artificial intelligence and law
The Growing Hierarchical Self-Organizing Map
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Neural and Wavelet Network Models for Financial Distress Classification
Data Mining and Knowledge Discovery
Data Mining techniques for the detection of fraudulent financial statements
Expert Systems with Applications: An International Journal
International Journal of Intelligent Systems in Accounting and Finance Management
Using GHSOM to construct legal maps for Taiwan's securities and futures markets
Expert Systems with Applications: An International Journal
Web mining based on Growing Hierarchical Self-Organizing Maps: Analysis of a real citizen web portal
Expert Systems with Applications: An International Journal
Using SOM and PCA for analysing and interpreting data from a P-removal SBR
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Exploring Fraudulent Financial Reporting with GHSOM
PAISI '09 Proceedings of the Pacific Asia Workshop on Intelligence and Security Informatics
Research on CBR system based on data mining
Applied Soft Computing
Neural fraud detection in credit card operations
IEEE Transactions on Neural Networks
Bankruptcy prediction for credit risk using neural networks: A survey and new results
IEEE Transactions on Neural Networks
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
A constructive algorithm for training cooperative neural network ensembles
IEEE Transactions on Neural Networks
Exploiting the self-organizing financial stability map
Engineering Applications of Artificial Intelligence
| Hi-index | 12.05 |
Fraudulent financial reporting (FFR) involves conscious efforts to mislead others regarding the financial condition of a business. It usually consists of deliberate actions to deceive regulators, investors or the general public that also hinder systematic approaches from effective detection. The challenge comes from distinguishing dichotomous samples that have their major attributes falling in the same distribution. This study pioneers a novel dual GHSOM (Growing Hierarchical Self-Organizing Map) approach to discover the topological patterns of FFR, achieving effective FFR detection and feature extraction. Specifically, the proposed approach uses fraudulent samples and non-fraudulent samples to train a pair of dual GHSOMs under the same training parameters and examines the hypotheses for counterpart relationships among their subgroups taking advantage of unsupervised learning nature and growing hierarchical structures from GHSOMs. This study further presents (1) an effective classification rule to detect FFR based on the topological patterns and (2) an expert-competitive feature extraction mechanism to capture the salient characteristics of fraud behaviors. The experimental results against 762 annual financial statements from 144 public-traded companies in Taiwan (out of which 72 are fraudulent and 72 are non-fraudulent) reveal that the topological pattern of FFR follows the non-fraud-central spatial relationship, as well as shows the promise of using the topological patterns for FFR detection and feature extraction.