Article2012

Detection of Financial Statement Fraud Using Evolutionary Algorithms

Matthew Alden · Daniel M. Bryan · Brenton J. Lessley · Arindam Tripathy

36 citations0/0 files storedJournal of Emerging Technologies in Accounting

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

ABSTRACT In this paper, we use a Genetic Algorithm (GA) and MARLEDA—a modern Estimation of Distribution Algorithm (EDA)—to evolve and train several fuzzy rule-based classifiers (FRBCs) to detect patterns of financial statement fraud. We find that both GA and MARLEDA demonstrate a better ability to classify unseen corporate data observations than those of a traditional logistic regression model, and provide validity for detecting financial statement fraud with Evolutionary Algorithms (EAs) and FRBCs. Using ten-fold cross-validation, the GA and MARLEDA yield average training classification accuracy rates of 75.47 percent and 74.26 percent, respectively, and average validation accuracy rates of 63.75 percent and 64.46 percent, respectively.

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 — 36 citations exist upstream.

References

2011Predicting Material Accounting Misstatements*2010Detection of financial statement fraud and feature selection using data mining techniques 1998Fraudulently Misstated Financial Statements and Insider Trading: An Empirical Analysis.2009Detecting and predicting financial statement fraud: The effectiveness of the fraud triangle and SAS No. 99 2011Using Financial Statement Data To Identify Factors Associated With Fraudulent Financial Reporting 1997Assessing the Risk of Management Fraud Through Neural Network Technology.2004Can financial ratios detect fraudulent financial reporting?2010A computational model for financial reporting fraud detection

Cited by

2021The effect of intellectual capital on fraud in financial statements 2023Detecting future financial statement fraud using a machine learning model in Indonesia: a comparative study 2024Predicting financial distress in TSX-listed firms using machine learning algorithms 2024The application of machine learning to study fraud in the accounting literature 2022Predicting earnings management through machine learning ensemble classifiers

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.