Data Mining techniques for the detection of fraudulent financial statements

By Efstathios Kirkos, Charalambos Spathis, Yannis Manolopoulos. 2007

731 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Computational Theory and Mathematics

How researchers see this paper

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

In Accounting

In Artificial Intelligence

In Computational Theory and Mathematics

Citations in the corpus

Deep Learning for Financial Applications : A Survey (2020, neutral): “[107], Yue et al.”
A Review of Financial Accounting Fraud Detection based on Data Mining Techniques (2012, neutral): “The research in [8] described that Bayesian belief network model correctly classified 90.3% of the validation sample for fraud detection.”
The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature (2011, methodology): “conditional independencies between the variables [45,57].”
Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare (2015, background): “These applications include healthcare [1], network intrusion detection systems[2], spam and fraud detection, phishing detection [3], political decision making [4], adversarial advertisement detection [5], and financial engineering [6].”
CARTMAP: a neural network method for automated feature selection in financial time series forecasting (2012, background): “, [6, 19, 23, 25]); this paper seeks to synergistically combine the two disciplines to solve a”
MetaFraud: A Meta-Learning Framework for Detecting Financial Fraud (2012, methodology): “The enhanced performance on manufacturing firms is consistent with previous work that has also attained good fraud detection rates on such data ( Kirkos et al. 2007; Spathis 2002).”
Big data techniques in auditing research and practice: Current trends and future opportunities (2018, neutral): “Meanwhile, Kirkos, Spathis, and Manolopoulos (2007) found a Bayesian network that outperformed an artificial neural network, as well as a decision tree.”
A Review of Data Mining-Based Financial Fraud Detection Research (2007, methodology): “Detection Algorithm Reference Regression [1, 3, 5, 8, 19, 24, 26, 27] Neural networks [13, 15, 16, 18, 20, 22] Statistical tests [4, 6, 7] Bayesian [20] Decision tree [20] Stacking variant methodology [21] Others non-DM based methods [9]”
Financial Statement Fraud Detection: An Analysis of Statistical and Machine Learning Algorithms (2011, neutral): “…of logistic regression will be better than if performance is measured using classiﬁcation results based on the default threshold of 0.5, which has been used in most prior fraud research (Fanning and Cogger 1998; Feroz et al. 2000; Lin et al. 2003; Kotsiantis et al. 2006; Kirkos et al. 2007).”
Intelligent financial fraud detection: A comprehensive review (2016, neutral): “The network performs by modelling the input variables as a layer of vertices and then assigning a weighting to each connection in the graph, while the other vertices are put into separate levels reflecting their distance from the input nodes [28] (see Figure 4).”
An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine (2020, methodology): “Bayesian belief networks (BNNs) and decision trees (DTs) were used in [14] to detect fraud in financial transactions.”
The Analysis of Fraudulent Financial Reporting Determinant through Fraud Pentagon Approach (2017, background): “Previous researches on fraud is still dominated by the fraud triangle model that are Kusumawardhani (2013), Ardiyani & Utaminingsih (2015), Abayomi (2016), Iqbal & Murtanto (2016), while Sihombing & Rahardjo (2014) examine Fraud diamond.”
Credit Card Fraud Detection Using AdaBoost and Majority Voting (2019, methodology): “Decision Trees (DT) and Bayesian Belief Networks (BNN) were used in [13] to identify financial statement fraud.”