Credit Card Risk Detection based on Feature-Filter and Fraud Identification

Credit card fraud can destabilise economies, reduce confidence between customers and banks and affect other individuals or companies negatively. The primordial objective of banks is to identify fraudulent transactions with a high level of accuracy to reduce the training time and the costs of the manual investigation activity. This paper proposes a credit card fraud detection method using Random Forest as dimensionality reduction algorithm and Isolation Forest as a fraud detection algorithm. The method is applied to a large dataset in purpose to investigate their fraud detection accuracy. The experimental results of this study confirms the advantages and effectiveness of the proposed method in different criteria: accuracy, sensitivity and training time.

[1]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[2]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD '00.

[3]  D. Hand,et al.  Unsupervised Profiling Methods for Fraud Detection , 2002 .

[4]  Yong Hu,et al.  The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature , 2011, Decis. Support Syst..

[5]  Xiangliang Zhang,et al.  A Novel Intrusion Detection Method Based on Principle Component Analysis in Computer Security , 2004, ISNN.

[6]  Charu C. Aggarwal,et al.  Outlier Analysis , 2013, Springer New York.

[7]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[8]  Longbing Cao,et al.  Effective detection of sophisticated online banking fraud on extremely imbalanced data , 2012, World Wide Web.

[9]  Joaquim F. Pinto da Costa,et al.  A Weighted Principal Component Analysis and Its Application to Gene Expression Data , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[10]  Hans-Peter Kriegel,et al.  LoOP: local outlier probabilities , 2009, CIKM.

[11]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[12]  Jian Tang,et al.  Enhancing Effectiveness of Outlier Detections for Low Density Patterns , 2002, PAKDD.

[13]  I. Bose,et al.  Data Mining For Detection Of Financial Statement Fraud In Chinese Companies , 2007 .

[14]  Michael T. Goodrich,et al.  Education forum: Web Enhanced Textbooks , 1998, SIGA.

[15]  Cesare Alippi,et al.  Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[16]  Yannis Manolopoulos,et al.  Data Mining techniques for the detection of fraudulent financial statements , 2007, Expert Syst. Appl..

[17]  Pourya Shamsolmoali,et al.  Application of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier , 2015 .

[18]  Montserrat Guillen,et al.  Selection Bias and Auditing Policies for Insurance Claims , 2007 .

[19]  Isti Surjandari,et al.  Data mining application to detect financial fraud in Indonesia's public companies , 2017, 2017 3rd International Conference on Science in Information Technology (ICSITech).

[20]  F. J. Arregui,et al.  Burst Detection in Water Networks Using Principal Component Analysis , 2012 .

[21]  Vadlamani Ravi,et al.  Detection of financial statement fraud and feature selection using data mining techniques , 2011, Decis. Support Syst..

[22]  Siddhartha Bhattacharyya,et al.  Data mining for credit card fraud: A comparative study , 2011, Decis. Support Syst..

[23]  William Perrizo,et al.  RDF: a density-based outlier detection method using vertical data representation , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[24]  Ke Zhang,et al.  A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data , 2009, PAKDD.

[25]  Neha Sethi,et al.  A Revived Survey of Various Credit Card Fraud Detection Techniques , 2014 .

[26]  Priya Ravindra Shimpi,et al.  Survey on Credit Card Fraud Detection Techniques , 2016 .

[27]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[28]  Zhi-Hua Zhou,et al.  Isolation Forest , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[29]  Bernhard Schölkopf,et al.  Support Vector Method for Novelty Detection , 1999, NIPS.

[30]  Gaurav Kapoor,et al.  Detecting evolutionary financial statement fraud , 2011, Decis. Support Syst..

[31]  M. Shyu,et al.  A Novel Anomaly Detection Scheme Based on Principal Component Classifier , 2003 .