Reference health

A survey of anomaly detection techniques in financial domain

https://doi.org/10.1016/j.future.2015.01.001
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33/33 checkable references clean · checked 2026-07-22

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

47 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 33 checked references that resolve
resolves10.1145/1541880.1541882
Anomaly detection
resolves10.1109/CIFER.1997.618940
CARDWATCH: a neural network based database mining system for credit card fraud detection
resolves10.1007/s10618-009-0146-1
COG: local decomposition for rare class analysis
resolves10.1016/j.comnet.2007.02.001
An overview of anomaly detection techniques: Existing solutions and latest technological trends
resolves10.1023/B:AIRE.0000045502.10941.a9
A Survey of Outlier Detection Methodologies
resolves10.1016/j.sigpro.2003.07.019
Novelty detection: a review—part 2:
resolves10.1109/ICCIS.2006.252287
A Comparative Study for Outlier Detection Techniques in Data Mining
resolves10.1109/GRC.2009.5255148
Combining self-organizing map and K-means clustering for detecting fraudulent financial statements
resolves10.1145/331499.331504
Data clustering
resolves10.1109/34.824819
Statistical pattern recognition: a review
resolves10.1023/A:1012801612483
On Clustering Validation Techniques
resolves10.1109/ICIEA.2013.6566435
A novel approach for outlier detection and clustering improvement
resolves10.1007/11499145_99
Improving K-Means by Outlier Removal
resolves10.1109/TKDE.2002.1033770
CLARANS: a method for clustering objects for spatial data mining
resolves10.1109/ICMTMA.2013.1
A K-harmonic Means Clustering Algorithm Based on Enhanced Differential Evolution
resolves10.1007/s00357-001-0004-3
K-modes Clustering
resolves10.1023/A:1009769707641
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
resolves10.2139/ssrn.1910468
Application of Anomaly Detection Techniques to Identify Fraudulent Refunds
resolves10.1109/ICDMW.2010.66
Application of Data Mining for Anti-money Laundering Detection: A Case Study
resolves10.1109/ICNIT.2010.5508564
Using clustering techniques to analyze fraudulent behavior changes in online auctions
resolves10.1145/1656274.1656278
The WEKA data mining software
resolves10.1109/5.58325
The self-organizing map
resolves10.1109/TKDE.2007.190725
An Efficient Clustering Scheme to Exploit Hierarchical Data in Network Traffic Analysis
resolves10.1016/S0306-4379(00)00022-3
Rock: A robust clustering algorithm for categorical attributes
resolves10.1016/j.dss.2010.08.010
A computational model for financial reporting fraud detection
resolves10.1109/79.543975
The expectation-maximization algorithm
resolves10.1109/ICIME.2010.5478070
Hybrid outlier mining algorithm based evaluation of client moral risk in insurance company
resolves10.1016/j.accinf.2009.12.004
Internal fraud risk reduction: Results of a data mining case study
resolves10.1109/CCDC.2011.5968986
Research on anti-money laundering based on core decision tree algorithm
resolves10.1016/j.inffus.2008.04.001
Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning
resolves10.1145/335191.335437
Efficient algorithms for mining outliers from large data sets
resolves10.1145/980972.980990
"In vivo" spam filtering
The 47 references without a DOI — listed, not checked
no DOI — not checkedOutlier detection
no DOI — not checkedApproximations to magic: finding unusual medical time series
no DOI — not checkedR.J. Bolton, D.J. H, Unsupervised profiling methods for fraud detection, in: Proc. Credit Scoring and Credit Control VII, 2001, pp. 5–7.
no DOI — not checked10.1016/j.future.2015.01.001_br000025
no DOI — not checkedNeural data mining for credit card fraud detection
no DOI — not checkedSurvey of clustering based financial fraud detection research
no DOI — not checkedA comprehensive survey of data mining-based fraud detection research
no DOI — not checkedOutliers
no DOI — not checkedAmerican Heritage Dictionary. URL: http://www.ahdictionary.com  (accessed 29.11.13).
no DOI — not checked10.1016/j.future.2015.01.001_br000085
no DOI — not checked10.1016/j.future.2015.01.001_br000090
no DOI — not checkedFBI: White Collar Crime. URL: http://www.fbi.gov  (accessed 29.11.13).
no DOI — not checked2013/2014 Global Fraud Report. URL: http://www.kroll.com  (accessed 29.11.13).
no DOI — not checkedEuropean ATM skimming machine your credit card’s new worst enemy in Australian crime first. URL: http://www.couriermail.com.au/news  (accessed 29.11.13).
no DOI — not checkedInsurance Fraud Bureau of Australia. URL: http://www.ifba.org.au  (accessed 29.11.13).
no DOI — not checkedEarly detection of insider trading in option markets
no DOI — not checked10.1016/j.future.2015.01.001_br000125
no DOI — not checked10.1016/j.future.2015.01.001_br000130
no DOI — not checked10.1016/j.future.2015.01.001_br000150
no DOI — not checkedDiscovering cluster based local outliers
no DOI — not checkedSome methods for classification and analysis of multivariate observations
no DOI — not checked10.1016/j.future.2015.01.001_br000175
no DOI — not checkedEfficient and effective clustering methods for spatial data mining
no DOI — not checkedClustering and the continuous k-means algorithm
no DOI — not checkedAcceleration of k-means and related clustering algorithms
no DOI — not checked10.1016/j.future.2015.01.001_br000200
no DOI — not checkedX-means: extending k-means with efficient estimation of the number of clusters
no DOI — not checkedk-means clustering with outlier detection, mixed variables and missing values
no DOI — not checkedCluster analysis for anomaly detection in accounting data: an audit approach
no DOI — not checkedN. Lybaert, M. Jans, K. Vanhoof, Data mining for fraud detection: toward an improvement on internal control systems? in: European Accounting Association—Annual Congress, 2010, pp. 1–27.
no DOI — not checkedSAS: Business Intelligence Software. URL: http://www.sas.com  (accessed 29.11.13).
no DOI — not checkedYahoo!Taiwan. URL: http://www.tw.yahoo.com  (accessed 29.11.13).
no DOI — not checkedANOVA Analysis. URL: http://www.csse.monash.edu.au/~smarkham/resources/anova.htm  (accessed 29.11.13).
no DOI — not checkedBIRCH: an efficient data clustering method for very large databases
no DOI — not checkedCURE: an efficient clustering algorithm for large databases
no DOI — not checked10.1016/j.future.2015.01.001_br000310
no DOI — not checkedL. Torgo, E. Lopes, Utility-based fraud detection, in: 22nd International Joint Conference on Artificial Intelligence, 2011, pp. 1517–1522.
no DOI — not checkedResource-bounded outlier detection using clustering methods
no DOI — not checkedA framework for internal fraud risk reduction at it integrating business processes: the IFR2 framework
no DOI — not checkedJ. Magidson, J.K. Vermunt, Latent Class Cluster Analysis, Statistical Innovations Inc., 2002.
no DOI — not checkedA data mining with hybrid approach based transaction risk score generation model (TRSGM) for fraud detection of online financial transaction
no DOI — not checkedK. Bache, M. Lichman, UCI Machine Learning Repository, 2013. URL: http://archive.ics.uci.edu/ml.
no DOI — not checkedA synthetic fraud data generation methodology
no DOI — not checked10.1016/j.future.2015.01.001_br000380
no DOI — not checked1999 KDD Cup Dataset. URL: www.kdd.ics.uci.edu  (accessed 21.12.13).
no DOI — not checkedThe full dataset derived from about a month’s background traffic and simulated attacks. URL: http://seit.unsw.adfa.edu.au/staff/sites/kshafi  (accessed 21.12.13).
no DOI — not checkedSynthesizing test data for fraud detection systems
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