Universal approximation using radial-basis-function networks
Neural Computation
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Minority report in fraud detection: classification of skewed data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving classifier utility by altering the misclassification cost ratio
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
One-Benefit learning: cost-sensitive learning with restricted cost information
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Maximum profit mining and its application in software development
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Test Strategies for Cost-Sensitive Decision Trees
IEEE Transactions on Knowledge and Data Engineering
ROC graphs with instance-varying costs
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
SIBGRAPI '07 Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing
A threshold varying bisection method for cost sensitive learning in neural networks
Expert Systems with Applications: An International Journal
Adaptive mixtures of local experts
Neural Computation
Association rules applied to credit card fraud detection
Expert Systems with Applications: An International Journal
Estimating the utility value of individual credit card delinquents
Expert Systems with Applications: An International Journal
Misclassification cost-sensitive fault prediction models
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Designing an expert system for fraud detection in private telecommunications networks
Expert Systems with Applications: An International Journal
An expert system for detecting automobile insurance fraud using social network analysis
Expert Systems with Applications: An International Journal
A data mining framework for detecting subscription fraud in telecommunication
Engineering Applications of Artificial Intelligence
A probabilistic approach to fraud detection in telecommunications
Knowledge-Based Systems
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As credit loan products significantly increase in most financial institutions, the number of fraudulent transactions is also growing rapidly. Therefore, to manage the financial risks successfully, the financial institutions should reinforce the qualifications for a loan and augment the ability to detect and manage a credit loan fraud proactively. In the process of building a classification model to detect credit loan frauds, utility from classification results (i.e., benefits from correct prediction and costs from incorrect prediction) is more important than the accuracy rate of classification. The objective of this paper is two-fold: (1) to propose a new approach to building a classification model for detecting credit loan fraud based on an individual-level utility, and (2) to suggest customized interest rate for each customer - from both opportunity utility and cash flow perspectives. Experimental results show that our proposed model comes up with higher utility than the fraud detection models which do not take into account the individual-level utility concept. Also, it is shown that the individual-level utility from our model is more accurate than the mean-level utility used in previous researches, from both opportunity utility and cash flow perspectives. Implications of the experimental results from both perspectives are provided.