Improving Credit Card Fraud Detection using a Meta-learning Strategy
One of the issues facing credit card fraud detection systems is that a significant percentage of transactions labeled as fraudulent are in fact legitimate. These “false alarms” delay the detection of fraudulent transactions. Analysis of 11 months of credit card transaction data from a major Canadian...
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ndltd-LACETR-oai-collectionscanada.gc.ca-OTU.1807-313962013-11-02T03:43:31ZImproving Credit Card Fraud Detection using a Meta-learning StrategyPun, Joseph King-Fungdata miningcredit cardmeta-learningartificial intelligencealgorithmsfraud detection0800One of the issues facing credit card fraud detection systems is that a significant percentage of transactions labeled as fraudulent are in fact legitimate. These “false alarms” delay the detection of fraudulent transactions. Analysis of 11 months of credit card transaction data from a major Canadian bank was conducted to determine savings improvements that can be achieved by identifying truly fraudulent transactions. A meta-classifier model was used in this research. This model consists of 3 base classifiers constructed using the k-nearest neighbour, decision tree, and naïve Bayesian algorithms. The naïve Bayesian algorithm was also used as the meta-level algorithm to combine the base classifier predictions to produce the final classifier. Results from this research show that when a meta-classifier was deployed in series with the Bank’s existing fraud detection algorithm a 24% to 34% performance improvement was achieved resulting in $1.8 to $2.6 million cost savings per year.Lawryshyn, Yuri Andrew2011-112011-12-19T19:07:19ZNO_RESTRICTION2011-12-19T19:07:19Z2011-12-19Thesishttp://hdl.handle.net/1807/31396en_ca |
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data mining credit card meta-learning artificial intelligence algorithms fraud detection 0800 |
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data mining credit card meta-learning artificial intelligence algorithms fraud detection 0800 Pun, Joseph King-Fung Improving Credit Card Fraud Detection using a Meta-learning Strategy |
description |
One of the issues facing credit card fraud detection systems is that a significant percentage of transactions labeled as fraudulent are in fact legitimate. These “false alarms” delay the detection of fraudulent transactions. Analysis of 11 months of credit card transaction data from a major Canadian bank was conducted to determine savings improvements that can be achieved by identifying truly fraudulent transactions. A meta-classifier model was used in this research. This model consists of 3 base classifiers constructed using the k-nearest neighbour, decision tree, and naïve Bayesian algorithms. The naïve Bayesian algorithm was also used as the meta-level algorithm to combine the base classifier predictions to produce the final classifier. Results from this research show that when a meta-classifier was deployed in series with the Bank’s existing fraud detection algorithm a 24% to 34% performance improvement was achieved resulting in $1.8 to $2.6 million cost savings per year. |
author2 |
Lawryshyn, Yuri Andrew |
author_facet |
Lawryshyn, Yuri Andrew Pun, Joseph King-Fung |
author |
Pun, Joseph King-Fung |
author_sort |
Pun, Joseph King-Fung |
title |
Improving Credit Card Fraud Detection using a Meta-learning Strategy |
title_short |
Improving Credit Card Fraud Detection using a Meta-learning Strategy |
title_full |
Improving Credit Card Fraud Detection using a Meta-learning Strategy |
title_fullStr |
Improving Credit Card Fraud Detection using a Meta-learning Strategy |
title_full_unstemmed |
Improving Credit Card Fraud Detection using a Meta-learning Strategy |
title_sort |
improving credit card fraud detection using a meta-learning strategy |
publishDate |
2011 |
url |
http://hdl.handle.net/1807/31396 |
work_keys_str_mv |
AT punjosephkingfung improvingcreditcardfrauddetectionusingametalearningstrategy |
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