利用深度學習預測審計失敗--以台灣為例

Abstract

利用台灣上市、櫃公司財務報表重編作為審計失敗的指標，並依照Fully Connected Feedforward Network架構架設深度學習模型，用以預測可能發生審計失敗的查核案件，並利用半監督式學習與Voting等方式強化預測效果。與對照組邏輯斯回歸模型相比，預測能力顯著提升。

Used financial statement restatements of Taiwanese lised companies as indicator of audit failure, and built a Deep learning models based on Fully Connected Feedforward Network framework to predict audit failure, then used semi-supervised learning and Noting methods to improve prediction outcome. The predictive ability was signigicantly improved compared with the logistic regression model of the control group.