Enhancing fraud detection and security in banking and E-Commerce with AI-powered identity verification systems
1 Bachelor of Business Administration (BBA in Finance), Northern University, Bangladesh.
2 Bachelor of Arts with Honours in Marketing Management, London School of Management Education, Ilford, England.
3 Bachelor of Science in Computer Science and Engineering, Dhaka International University, Bangladesh.
4 Bachelor of Arts with Honours in English, National University, Bangladesh.
Research Article
World Journal of Advanced Research and Reviews, 2020, 06(03), 313-322
Publication history:
Received on 14 May 2020; revised on 20 June 2020; accepted on 28 June 2020
Abstract:
The ever-increasing digital banking and e-commerce has increased the financial frauds done using client-side cultural IP protocol. These systems find it difficult to catch up to the increasing transaction complexity. 'Fraud detection is vital for banks and e-commerce systems to prevent losses and protect users, so it's important for the computer systems to be sound in the digital age. In this paper AI based fraud detection system using machine learning algorithms such as Logistic Regression, Random Forest, Gradient Boosting (GBM), XGBoost and Light GBM have been suggested. To handle the class imbalance, we use SMOTE (Synthetic Minority Over-sampling Technique) for balancing the dataset and enhancing model performance. The accuracy, precision, recall, and F1-score were computed to evaluate the models. Results indicated that Gradient Boosting and LightGBM obtained the best performances when SMOTE was used to augment fraud detection. Our finding proved the success of AI and ML as fraud-detecting technique. SMOTE, being a method to overcome class-imbalance is incorporated but more fine-tuning should be done to combat changing fraudulent strategies. Dynamic model updating and feature engineering are needed to ensure the robustness of fraud detection systems in digital financial services applications.
Keywords:
AI-driven fraud detection; Banking transactions; Identity verification; E-commerce security; SMOTE; Fraud prevention; Real-time transaction
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Copyright © 2020 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0