Artificial Immune System and Machine Learning: A Synergistic Approach for Banking Fraud Detection

The rapid development of technology has significantly transformed sectors such as banking and finance. With financial transactions becoming more complex and frequent, the need for advanced fraud detection and prevention systems has become critical. This paper explores how artificial immune systems (AIS) and machine learning can enhance fraud detection in banking. Inspired by the human immune system, AIS offers innovative methods to identify and reduce fraud, ensuring the security of financial transactions. The paper emphasizes the crucial role of artificial intelligence in this transformation. The focus is on a proposed model that combines AIS, SMOTE (Synthetic Minority Oversampling Technique), and XGBoost to improve fraud detection efficiency. When applied to a dataset comprising both fraudulent and non-fraudulent banking operations, the model yielded promising results.