Topological pattern discovery and feature extraction for fraudulent financial reporting
We discover the spatial relationships of fraud and non-fraud financial statements. An expert-competitive feature extraction mechanism is presented to capture the salient characteristics of fraud behaviors. We design a parameter adjustment method to develop the classifiers in topological space. The proposed method can generate classification rules to help detect fraudulent samples.
fraudulent financial reporting unsupervised learning growing hierarchical self-organizing map data mining