Top articles for main contexts in financial fraud detection
| Context no. | Citation | Citation rate | Title | Author | Year |
|---|---|---|---|---|---|
| 2 | 678 | 96.85 | Graph based anomaly detection and description: A survey | Akoglu L., et al. | 2015 |
| 1 | 166 | 55.33 | Real-time big data processing for anomaly detection: A Survey | Habeeb R.A., et al. | 2019 |
| 2 | 32 | 32 | An Integrated Cluster Detection, Optimization, and Interpretation Approach for Financial Data | Li T., Kou G., Peng Y., Yu P.S. | 2021 |
| 2 | 171 | 28.5 | Intelligent financial fraud detection: A comprehensive review | West J., Bhattacharya M. | 2016 |
| 1 | 45 | 22.5 | Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach | Bao Y., et al. | 2020 |
| 2 | 154 | 15.4 | The evolution of fraud theory | Dorminey J., et al. | 2012 |
| 4 | 36 | 12 | The role of audit in the fight against corruption | Jeppesen | 2019 |
| 4 | 67 | 9.57 | Materiality guidance of the major public accounting firms | Eilifsen A., Messier W.F., Jr. | 2015 |
| 4 | 99 | 9 | Financial statement fraud detection: An analysis of statistical and machine learning algorithms | Perols J. | 2011 |
| 1 | 169 | 8.89 | Earnings Manipulation in Failing Firms | Rosner R.L. | 2003 |
| 1 | 53 | 8.83 | Unsupervised learning for robust Bitcoin fraud detection | Monamo P., et al. | 2016 |
| 3 | 26 | 8.66 | Situ: Identifying and explaining suspicious behavior in networks | Goodall J.R., et al. | 2019 |
| 3 | 33 | 8.25 | Malware analysis and detection using data mining and machine learning classification | Chowdhury M., et al. | 2018 |
| 4 | 96 | 8 | The world has changed - Have analytical procedure practices? | Trompeter G., Wright A. | 2010 |
| 3 | 17 | 5 | A flow-based approach for Trickbot banking trojan detection | Gezer A., et al. | 2019 |
| 3 | 12 | 4 | Stock Price Manipulation Detection using Generative Adversarial Networks | Leangarun T., et al. | 2019 |
| Context no. | Citation | Citation rate | Title | Author | Year |
|---|---|---|---|---|---|
| 2 | 678 | 96.85 | Graph based anomaly detection and description: A survey | Akoglu L., | 2015 |
| 1 | 166 | 55.33 | Real-time big data processing for anomaly detection: A Survey | Habeeb R.A., | 2019 |
| 2 | 32 | 32 | An Integrated Cluster Detection, Optimization, and Interpretation Approach for Financial Data | Li T., Kou G., Peng Y., Yu P.S. | 2021 |
| 2 | 171 | 28.5 | Intelligent financial fraud detection: A comprehensive review | West J., Bhattacharya M. | 2016 |
| 1 | 45 | 22.5 | Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach | Bao Y., | 2020 |
| 2 | 154 | 15.4 | The evolution of fraud theory | Dorminey J., | 2012 |
| 4 | 36 | 12 | The role of audit in the fight against corruption | Jeppesen | 2019 |
| 4 | 67 | 9.57 | Materiality guidance of the major public accounting firms | Eilifsen A., Messier W.F., Jr. | 2015 |
| 4 | 99 | 9 | Financial statement fraud detection: An analysis of statistical and machine learning algorithms | Perols J. | 2011 |
| 1 | 169 | 8.89 | Earnings Manipulation in Failing Firms | Rosner R.L. | 2003 |
| 1 | 53 | 8.83 | Unsupervised learning for robust Bitcoin fraud detection | Monamo P., | 2016 |
| 3 | 26 | 8.66 | Situ: Identifying and explaining suspicious behavior in networks | Goodall J.R., | 2019 |
| 3 | 33 | 8.25 | Malware analysis and detection using data mining and machine learning classification | Chowdhury M., | 2018 |
| 4 | 96 | 8 | The world has changed - Have analytical procedure practices? | Trompeter G., Wright A. | 2010 |
| 3 | 17 | 5 | A flow-based approach for Trickbot banking trojan detection | Gezer A., | 2019 |
| 3 | 12 | 4 | Stock Price Manipulation Detection using Generative Adversarial Networks | Leangarun T., | 2019 |
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