The paper "AI-POWERED FRAUD DETECTION IN BANKING: SAFEGUARDING FINANCIAL TRANSACTIONS" by Fatema Tuz Johora, Rakibul Hasan, Jahanara Akter, Sayeda Farjana Farabi, and Md Abdullah Al Mahmud explores the challenges and solutions in fraud detection within the banking sector. The authors highlight the increasing reliance on digital platforms and the rise of cyber threats, particularly during the COVID-19 pandemic, which have exacerbated the issue of bank fraud. They emphasize the need for more adaptive and efficient fraud detection methods, such as machine learning algorithms, to enhance security and trust in financial transactions. The study focuses on developing and evaluating machine learning models for identifying fraudulent banking transactions. It employs various algorithms, including Random Forest, K-Nearest Neighbor (KNN), Naive Bayes, Decision Trees, and Logistic Regression. The research contributes to the field by developing tailored models and implementing innovative preprocessing techniques to improve detection accuracy. The results show that logistic regression and decision tree models achieve impressive accuracy and AUC values of approximately 0.98 and 0.97, respectively. The authors discuss the importance of continuous learning and dynamic strategies to counter evolving cyber threats. They also address ethical and practical challenges, such as algorithm transparency and privacy concerns, which must be carefully considered in the implementation of AI-based fraud detection systems. The study concludes that the use of artificial intelligence in fraud detection is critical for enhancing security and trust in the financial ecosystem, especially in the context of increased online transactions and charitable activities.The paper "AI-POWERED FRAUD DETECTION IN BANKING: SAFEGUARDING FINANCIAL TRANSACTIONS" by Fatema Tuz Johora, Rakibul Hasan, Jahanara Akter, Sayeda Farjana Farabi, and Md Abdullah Al Mahmud explores the challenges and solutions in fraud detection within the banking sector. The authors highlight the increasing reliance on digital platforms and the rise of cyber threats, particularly during the COVID-19 pandemic, which have exacerbated the issue of bank fraud. They emphasize the need for more adaptive and efficient fraud detection methods, such as machine learning algorithms, to enhance security and trust in financial transactions. The study focuses on developing and evaluating machine learning models for identifying fraudulent banking transactions. It employs various algorithms, including Random Forest, K-Nearest Neighbor (KNN), Naive Bayes, Decision Trees, and Logistic Regression. The research contributes to the field by developing tailored models and implementing innovative preprocessing techniques to improve detection accuracy. The results show that logistic regression and decision tree models achieve impressive accuracy and AUC values of approximately 0.98 and 0.97, respectively. The authors discuss the importance of continuous learning and dynamic strategies to counter evolving cyber threats. They also address ethical and practical challenges, such as algorithm transparency and privacy concerns, which must be carefully considered in the implementation of AI-based fraud detection systems. The study concludes that the use of artificial intelligence in fraud detection is critical for enhancing security and trust in the financial ecosystem, especially in the context of increased online transactions and charitable activities.