Data mining enhances fraud detection by analyzing transaction logs, user behaviour, and historical #fraud records. It identifies normal #patterns, such as spending habits and login times, to detect anomalies like unusual transactions or logins from unfamiliar locations.
#algorithms like Isolation Forest and SVM help flag suspicious activities, while #predictivemodels—Decision Trees, Random Forests, and Neural Networks—improve accuracy using historical fraud data. Real-time monitoring with tools like #ApacheKafka and #spark Streaming ensures instant alerts for fraudulent activities.
Continuous learning allows models to adapt to evolving fraud tactics, enhancing detection over time. By integrating these techniques, #data mining strengthens fraud prevention, mitigates financial risks, and improves #security.
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