A replication of a cost-sensitive decision tree approach for fraud detection

Payment card fraud poses a problem with widespread implications. However, the class imbalance between genuine and fraudulent transactions presents a challenge to traditional learning methods. Cost-based methods address this problem by assigning different costs to the misclassification of each class. Sahin et al. [7] define a cost-sensitive decision tree algorithm, and this study attempts to both replicate and build on their findings. The results presented here do not support the hypothesis that the cost-sensitive algorithm provides an improvement over traditional decision tree algorithms. However, the results do suggest that different methods of prioritizing alerted transactions can lead to performance improvements over some measures.

Author dc:creator

• Kristófer Reynisson 1995-

Contributors dc:contributor

• Háskólinn í Reykjavík

• Tölvunarfræði
• Greiðslukort
• Afbrot
• Gervigreind
• Computer science
• Credit cards
• Crime
• Artificial intelligence

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/1946/34915

OAI identifier oai:identifier

oai:skemman.is:1946/34915