Detection of financial statement fraud and feature selection using data mining techniques

By P. Ravisankar, Vadlamani Ravi, G. Rao, Indranil Bose. Decision Support Systems, 2011

545 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/ec3ab5abe884707aae424d841673df00c807cb7d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Peng Cui
2. Daixin Wang
3. Christine Dewi
4. Manuel Graña
5. Michal Wozniak
6. Asoke Kumar Nandi
7. Rung Ching Chen
8. Chee Peng Lim

In Management Science and Operations Research

1. Peng Cui
2. Manuel Graña
3. Michal Wozniak
4. Chee Peng Lim
5. Manjeevan Seera
6. Yang Bao
7. Jun Zhou
8. Bin Ke

Citations in the corpus

• A survey of multiple classifier systems as hybrid systems (2014, background): “ported in [198] to detect fraud in the financial statement of big companies.”
• A Review of Financial Accounting Fraud Detection based on Data Mining Techniques (2012, unknown): “Some of the recent study in the financial fraud detection make used of two or more data mining applications in a hybrid manner or just attempt to compare their effectiveness [70-72].”
• Credit Card Fraud Detection Using AdaBoost and Majority Voting (2019, methodology): “The results indicated that the PNN performed the best, which was followed by GP [12].”
• Selecting critical features for data classification based on machine learning methods (2020, background): “…distress [66], Household indebt [67], social vulnerability [68], construction of social vulnerability index [69], financial statement fraud [70], insurance fraud [71], macroeconomic Influencers [72], stock markets [73] model-based approach is more closely tied to the model performance and…”
• A Semi-Supervised Graph Attentive Network for Financial Fraud Detection (2019, methodology): “[9] extracts some statistic features about assets, liabilities, incomes, debt and sales and uses a multi-layer feedforward network to do financial statement fraud detection.”
• Deep-learning inversion: a next generation seismic velocity-model building method (2019, background): “, 2000), fraud alerts (Ravisankar et al., 2011), and other applications.”
• Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach (2020, background): “Machine learning has been applied in computational finance (Li and Hoi, 2014), among which one of the most interesting and challenging ones is the detection of accounting frauds in publicly traded firms (Ravisankar et al., 2011; Abbasi et al., 2012).”
• Big data techniques in auditing research and practice: Current trends and future opportunities (2018, unknown): “For example, Read and Yezegel (2016) use logistic regression to analyze the relationship between audit tenure and audit reporting.”
• Intelligent financial fraud detection: A comprehensive review (2016, unknown): “For binary classification the response variable takes the form: ( 2 ) And the formula for calculating that a sample belongs in class is given by: ( 3 ) Where and w are the regression tuning parameters representing the intercept and coefficient vector respectively [38].”