Assessing the Risk of Management Fraud Through Neural Network Technology.

Brian Patrick Green; Jae Hwa Choi

doi:10.2308/ajpt-9708303198

March 1, 1997Auditing A Journal of Practice & Theory

Assessing the Risk of Management Fraud Through Neural Network Technology.

View at Publisher

Ask AI

Bookmark

Share

Key Points

This research aims to develop a neural network model for detecting management fraud through financial data analysis.
Developed a neural network fraud classification model using endogenous financial data.
Evaluated the detection of financial errors and irregularities in financial statements.
Detailed methodology for the neural network model development was described.
The neural network model effectively classified financial fraud with higher accuracy rates.
Improvements in detecting irregularities helped identify hidden issues in financial statements.

Implication

Develops a neural network model to classify fraud in financial data, suggesting improved detection methods.

Abstract

Develops an effective neural networks (NN) fraud classification model employing endogenous financial data. How financial errors and irregularities in financial statements are detected; Neural networks described; Methodology used in NN model development study; Results and implications of the study.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Green et al. (1997) studied this question.

synapsesocial.com/papers/6a58b07d94f2b55f8f4ccee8 https://doi.org/10.2308/ajpt-9708303198

View at Publisher

Ask AI

Bookmark

Share

Assessing the Risk of Management Fraud Through Neural Network Technology.

Key Points

Implication

Abstract

Discussion

Cite This Study

Assessing the Risk of Management Fraud Through Neural Network Technology.

Key Points

Implication

Abstract

Discussion

Cite This Study

Keep reading

Keep reading