A cost-sensitive decision tree approach for fraud detection

By Yusuf Sahin, Serol Bulkan, E. Duman. Expert systems with applications, 2013

378 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/fb8ab6df916788f3c6953760ee23d94c1df00fa7

How researchers see this paper

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

In Accounting

1. Gianluca Bontempi
2. Harsurinder Kaur
3. Husanbir Singh Pannu
4. Avleen Kaur Malhi
5. Andrea Dal Pozzolo
6. John Awoyemi
7. Adebayo Olusola Adetunmbi
8. Olivier Caelen

In Artificial Intelligence

1. Gianluca Bontempi
2. Andrea Dal Pozzolo
3. Olivier Caelen
4. Philip S. Yu
5. Cesare Alippi
6. Giacomo Boracchi
7. Yingtong Dou
8. Hao Peng

In Electrical and Electronic Engineering

1. Gianluca Bontempi
2. Andrea Dal Pozzolo
3. Philip S. Yu
4. Asoke Kumar Nandi
5. Cesare Alippi
6. Harsurinder Kaur
7. Husanbir Singh Pannu
8. Avleen Kaur Malhi

Citations in the corpus

• Credit card fraud detection using machine learning techniques: A comparative analysis (2017, positive): “Stratified sampling is also applied in [21].”
• Fraud detection system: A survey (2016, methodology): “For example, Sahin et al. (2013) used cost-sensitive classi fi ers in addressing the class imbalance problem.”
• Adaptive Machine Learning for Credit Card Fraud Detection (2015, methodology): “[70] and Sahin et al.[68] use exampledependent cost-sensitive decision trees for maximizing the savings.”
• Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters (2020, background): “nly 5% of their neighbors have the same label. This is due to the class-imbalance nature of fraud problems and the relation camouflage of fraudsters. According to the cost-sensitive learning research [30], misclassifying a fraudster has a much higher cost to defenders than misclassifying a benign en”
• Intelligent financial fraud detection: A comprehensive review (2016, neutral): “Sahin et al. studied the ability of decision trees to identify fraudulent credit card transactions, using a six-month sample from a major bank [40].”
• Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy (2018, background): “In credit card fraud detection, the cost of a missed fraud is often assumed to be proportional to the transaction amount [6], [47], [55], and this assigns a larger misclassification cost to frauds, thus steering the classifier to prefer false alerts rather than taking the risk of missing a fraud.”
• Credit Card Fraud Detection Using AdaBoost and Majority Voting (2019, methodology): “INTRODUCTION Fraud is a wrongful or criminal deception aimed to bring financial or personal gain [1].”
• A Systematic Review on Imbalanced Data Challenges in Machine Learning (2019, background): “In Zakaryazad and Duman (2016) (Mardani and Shahriari 2013) and Sahin et al. (2013), proposed various approaches for detection of credit card fraud.”