Detecting Accounting Frauds in Publicly Traded U.S. Firms: A Machine Learning Approach

Asian Conference on Machine Learning Pub Date : 2020-03-01 DOI:10.2139/SSRN.2670703

Bin Li, Julia Yu, Jie Zhang, B. Ke

引用次数: 17

Abstract

This paper studies how machine learning techniques can facilitate the detection of accounting fraud in publicly traded US rms. Existing studies often mimic human experts and employ the nancial or nonnancial

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

美国上市公司的会计欺诈检测:机器学习方法

本文研究了机器学习技术如何促进美国上市公司会计欺诈的检测。现有的研究经常模仿人类专家，并采用金融或非金融手段

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Asian Conference on Machine Learning

自引率

0.00%

发文量

期刊最新文献

RoLNiP: Robust Learning Using Noisy Pairwise Comparisons AIIR-MIX: Multi-Agent Reinforcement Learning Meets Attention Individual Intrinsic Reward Mixing Network On the Interpretability of Attention Networks Evaluating the Perceived Safety of Urban City via Maximum Entropy Deep Inverse Reinforcement Learning One Gradient Frank-Wolfe for Decentralized Online Convex and Submodular Optimization