Financial Feature Embedding with Knowledge Representation Learning for Financial Statement Fraud Detection

Yuming Shen, Caichan Guo, Huan Li, Junjie Chen, Yunchuan Guo, Xin Ying Qiu

Abstract:

Identifying fraudulent financial statement is critical for capital market regulation and is generally formulated as a classification
problem. Feature selection in traditional machine learning methods does not consider correlation information among financial
features which may influence performance of classifiers. To explore correlation information on conducting financial statement
fraud detection (FSFD), we combine traditional features with knowledge graph models, and learn new representations enriched
with feature embedding of various financial categories. These feature relations defined by correlation types may form knowledge
graphs with features as nodes and correlation relations as edges. Experimentations demonstrate that financial feature representations
with correlation information significantly improve classification performances for SVM and K-NN, marginally better than decision
trees and logistic regression, but not outperforming naive Bayes (Kernel).