| dc.description.abstract | The world is reaching a cashless
society with the increment of non-cash
transactions. E-commerce has become an
essential factor in every organization in global
trade. Since financial institutions co-operate with
billions of online transactions per day,
identifying fraudulent transactions has become a
challenge. This research was mainly focused on
identifying the best intelligent adaptive
authentication technique for credit card fraud
detection. Areal-world transaction dataset of
European credit cardholders and a synthetic
dataset were used to extract the historical
transactional patterns using Artificial Neural
Network (ANN). Different classification
algorithms, Logistic Regression, Decision Tree,
Random Forest and XGBoost were also used for a
comparative analysis to classify a real-world
dataset. Among all, ANN and XGBoost have
shown the highest performance in the binary
classification of fraud and legitimate
transactions. ANN has shown an accuracy of
99.94% and high adaptability in handling large
datasets, by giving zero misclassification of fraud
as a legitimate transaction by reducing the risk to
its minimum. | en_US |
