Abstract:
Financial fraud is a growing concern with far-reaching concerns in financial institutions,
government, and corporate organizations, leading to substantial monetary
losses. The primary cause of financial loss is credit card fraud; it affects issuers
and clients, which is a significant threat to the business as clients will run to their
competitors, wherein they will feel secure. Solving fraud problems is beyond human
capability, so financial institutions can utilize machine learning algorithms to
detect fraudulent behaviour by learning through credit card transactions. This thesis
develops the boosted random forest, integrating an adaptive boosting algorithm
into a random forest algorithm, such that the performance of a model is improved in
predicting credit card fraudulent transactions. The confusion matrix is used to evaluate
the performance of the models, wherein random forest, adaptive boosting and
boosted random forest were compared. The results indicated that the boosted random
forest outperformed the individual models with an accuracy of 99.9%, which
corresponded with the results from confusion matrix. However random forest and
adaptive boosting had 100% and 99% respectively, which did not correspond to the
results on confusion matrix, meaning the individual models need to be more accurate.
Thus, by implementing the proposed approach to a credit card management
system, financial loss will be reduced to a greater extent.
