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.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OTU.1807/31396 |
| Date | 19 December 2011 |
| Creators | Pun, Joseph King-Fung |
| Contributors | Lawryshyn, Yuri Andrew |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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