Approaches To Fraud Detection On Credit Card Transactions Using Artificial
Keywords:
Credit Card Fraud, Fraud Detection, Artificial Intelligence, Machine Learning, Deep Learning, Neural Networks, Anomaly Detection, Financial Security, Data Privacy, Transaction Monitoring, Supervised Learning, Unsupervised Learning, Fraud Prevention, Real-Time Detection, Cybersecurity.Abstract
Credit card fraud continues to pose serious financial risks. Therefore, advanced fraud detection methods are required. This study evaluates AI-based models for detection of fraud, such as machine learning, deep learning and hybrid approaches, and compares them to traditional rule-based systems. Reports suggest that AI does enhance fraud detection significantly, with great amounts of false positives being avoided. However, there remain certain challenges: data privacy concerns, evolving techniques for perpetrating fraud, and the issue of explainability. The case studies incorporate a general overview of how AI affects reality, according to financial institutions. Further improvement includes research onto model adaptability, incorporation into blockchain and transparency through to the strengthening of fraud prevention. This study also highlighted the significant capabilities of AI for securing digital transactions and reducing financial fraud risk in general.
