Python
Aim
To develop a robust fraud detection system for e-commerce transactions by leveraging machine learning algorithms on simulated BANKSIM data, achieving high classification accuracy to mitigate risks associated with fraudulent transactions.
Abstract
E-commerce has rapidly evolved into a cornerstone of modern commerce, enabling convenient transactions for consumers. However, the increase in online transactions has also led to a surge in fraudulent activities, posing significant risks to both consumers and businesses. This paper presents a pilot study focused on detecting fraudulent transactions through machine learning techniques. Utilizing the BANKSIM dataset, we explore various algorithms, including Gaussian Naïve Bayes, K-Nearest Neighbors (K-NN), and Fine Tree. Our experiments demonstrate that the Fine Tree algorithm achieves the highest accuracy of 99.9% with an F1-Score of 0.99, indicating its efficacy in classifying legitimate versus fraudulent transactions. This study highlights the importance of machine learning in enhancing security measures for e-commerce operations.
Existing System
Current approaches to detecting fraudulent transactions in e-commerce typically involve rule-based systems or simplistic machine learning models that often fail to adapt to evolving fraud tactics. While some models have achieved reasonable performance, they frequently suffer from high false positive rates, leading to increased operational costs and a loss of consumer trust. Additionally, traditional methods may overlook subtle patterns indicative of fraud due to the complexity of transaction data. This underscores the need for advanced techniques that can effectively analyze transactional behavior and improve detection accuracy.
Proposed System
The proposed system aims to significantly enhance the detection of fraudulent transactions in e-commerce through a comprehensive machine learning framework.
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