Articlehybrid2015

Financial Fraud Detection Model: Based on Random Forest

165 citations0/0 files storedInternational Journal of Economics and Finance

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Financial Distress and Bankruptcy Prediction Imbalanced Data Classification Techniques Benford’s Law and Fraud Detection

Abstract

Business’s accelerated globalization has weakened regulatory capacity of the law and scholars have been paid attention to fraud detection in recent years. In this study, we introduced Random Forest (RF) for financial fraud technique detection and detailed features selection, variables’ importance measurement, partial correlation analysis and Multidimensional analysis. The results show that a combination of eight variables has the highest accuracy. The ratio of debt to equity (DEQUTY) is the most important variable in the model. Moreover, we applied four statistic methodologies, including parametric and non-parametric models to construct detection models and concluded that Random Forest has the highest accuracy and the non-parametric models have higher accuracy than non-parametric models. However, Random Forest can improve the detection efficiency significantly and have an important practical implication.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 165 citations exist upstream.

References

2005Ownership structure, corporate governance, and fraud: Evidence from China 2004THE CORPORATE GOVERNANCE MOSAIC AND FINANCIAL REPORTING QUALITY 2010Detection of financial statement fraud and feature selection using data mining techniques 2000Auditor Selection and Audit Committee Characteristics 1998Fraudulently Misstated Financial Statements and Insider Trading: An Empirical Analysis.2002Detecting false financial statements using published data: some evidence from Greece 2011Using Financial Statement Data To Identify Factors Associated With Fraudulent Financial Reporting

Cited by

2022An Analysis on Financial Statement Fraud Detection for Chinese Listed Companies Using Deep Learning 2021Application of gray DEMATEL-ANP in green-strategic sourcing 2022Psychological consumer behavior and sustainable green food purchase 2018A financial statement fraud detection model based on hybrid data mining methods 2019Foreign direct investment and economic growth in South America 2019Detecting Fraudulent Financial Statements for the Sustainable Development of the Socio-Economy in China: A Multi-Analytic Approach

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.