Publication Type

Journal Article

Version

publishedVersion

Publication Date

7-2024

Abstract

This study aims to assess the financial statement fraud risk ex ante and empirically explore its information content to help improve decision-making and daily operations. We propose an ex-ante fraud risk index by adopting an ensemble learning approach and a theoretically grounded framework. Our ensemble learning model systematically examines the fraud process and deals effectively with the unique challenges in the financial fraud setting, which yields superior prediction performance. More importantly, we empirically examine the information content of our estimated ex-ante fraud risk from the perspective of operational efficiency. Our empirical results find that the estimated ex-ante fraud risk is negatively correlated with sustaining operational efficiency. This study redefines fraud detection as an ongoing endeavor rather than a retrospective event, thus enabling managers and stakeholders to reconsider their operation decisions and reshape their entire operation processes accordingly.

Keywords

Decisions makings, Ensemble learning, Ensemble learning approach, Ex antes, Ex-ante fraud risk, Feature engineerings, Financial statement frauds, Fraud risk, Information contents, Operational efficiencies

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Data Science and Engineering; Information Systems and Management

Publication

Decision Support Systems

Volume

182

ISSN

0167-9236

Identifier

10.1016/j.dss.2024.114231

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.dss.2024.114231

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