The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature

By Eric Wai Ting Ngai, Yong Hu, Y. Wong, Yijun Chen, Xin Sun. 2011

1,300 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Information Systems, Evaluation & Benchmarks

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Citations in the corpus

Universal Language Model Fine-tuning for Text Classification (2018, background): “…is a category of Natural Language Processing (NLP) tasks with real-world applications such as spam, fraud, and bot detection (Jindal and Liu, 2007; Ngai et al., 2011; Chu et al., 2012), emergency response (Caragea et al., 2011), and commercial document classiﬁcation, such as for legal discovery…”
Fine-tuned Language Models for Text Classification (2018, background): “…of Natural Language Processing (NLP) tasks with many important real-world applications such as spam, fraud, and bot detection (Jindal and Liu, 2007; Ngai et al., 2011; Chu et al., 2012), emergency response (Caragea et al., 2011), and commercial document classification, such as for legal discovery…”
Deep Learning for Financial Applications : A Survey (2020, neutral): “[111], Sharma et al.”
Label-Only Membership Inference Attacks (2020, background): “Machine learning algorithms are often trained on sensitive or private user information, such as medical records [1, 2], textual conversations [3, 4], or financial information [5, 6].”
Ensemble learning for credit card fraud detection (2018, background): “In what follows, we mention a few important points: • Unavailability of Datasets: One major challenge associated with the problem is the lack of availability of public datasets [12, 13, 18].”
Corporate residence fraud detection (2014, background): “A comprehensive overview of the complete fraud detection literature is beyond the scope of this paper (see [4, 26, 29]).”
Data mining techniques and applications — A decade review (2017, background): “These all types of fraud are related to financial fraud [25].”
Anomaly Detection in Streams with Extreme Value Theory (2017, background): “The issue is that nowadays, more and more critical applications rely on high throughput streaming numerical data like energy management [30], cyber-security [32] or finance [26].”
Adaptive Machine Learning for Credit Card Fraud Detection (2015, methodology): “46 3.4 Algorithmic solutions for Fraud Detection . . . . . . . . . . . . . . . . . . 47 3.4.1 Supervised Approaches . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.4.2 Unsupervised Approaches . . . . . . . . . . . . . . . . . . . . . . . 51”
Rethinking Graph Neural Networks for Anomaly Detection (2022, background): “As a well-established problem, anomaly detection has received much attention due to its vast applicable tasks, e.g., cyber security (Ten et al., 2011), fraud detection (Ngai et al., 2011), health monitoring (Bao et al., 2019), device failure detection (Sipple, 2020), to name a few.”
Sequence classification for credit-card fraud detection (2018, background): “In a systematic review of 49 journal articles [Ngai et al., 2011] showed that Decision Trees, Neural Networks, Logistic Regression and Support Vector Machines have been the methods of choice among the many available methods.”
Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation (2015, methodology): “The review of Ngai et al. (2011) devoted attention to financial fraud detection, and classified 49 articles depending on the type of fraud.”
A Review of Financial Accounting Fraud Detection based on Data Mining Techniques (2012, neutral): “Economically, financial fraud is becoming an increasingly serious problem and effective detecting accounting fraud has always been an important but complex task for accounting professionals [7].”