Credit Card Fraud Detection Analysis using Different Machine Learning Models
Faculty Mentor
Dr. Kim Jongyeop
Location
Poster 211
Session Format
Poster Presentation
Keywords
Allen E. Paulson College of Engineering and Computing Student Research Symposium, Artificial Neural Network, ANN, Support Vector Machine, SVM, Genetic Algorithm, GA
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Presentation Type and Release Option
Presentation (File Not Available for Download)
Start Date
2022 12:00 AM
January 2022
Credit Card Fraud Detection Analysis using Different Machine Learning Models
Poster 211
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From the moment the e-commerce payment systems came to existence, there have always been people who will find new ways to access someone’s finances illegally. There has been an exponential growth of the internet as all transactions can easily be completed online by only entering your credit card information. Fraudsters have also increased activities to attack transactions that are made using credit cards.
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Machine Learning methods are implemented for credit card fraud detection. Credit card fraud is defined as a fraudulent transaction that is made using a credit or debit card by an unauthorized user. It is crucial to implement an effective credit card fraud detection method that can protect users from financial loss.
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Credit card fraud detection is challenging because of the constant changing pattern of the fraudulent transactions. Credit card fraud dataset is highly skewed in nature. Here is how the dataset looks like and the summary of the data:
