Abstract
As Financial Crimes Have Grown In Complexity, It Has Become Clear That Regular Rule-Based Aml Solutions Have Their Limits. It Describes A Pipeline Designed For Aml Detection Using Hadoop For Maintaining Data In Several Nodes, Pyspark To Process Data Quickly And Machine Learning For Making Predictions. The System Is Structured To Manage A Lot Of Financial Information, Boost The Accuracy Of Detection And Comply With Regulations Through The Use Of Ai Methods That Can Be Explained. By Using Ensemble Classifiers And Addressing Unbalanced Data With Smote, The Entire System Achieves Good Precision And Recall. Real-Time Monitoring And A Modular System Help Make Operations More Efficient And Allow Them To React To New Threats More Effectively. With Shap Values, We Can Be Sure That The Results Are Clear And Responsible For Their Use In Practice. Keyword: Aml Detection, Hadoop, Pyspark, Machine Learning, Real-Time Analytics, Shap, Scalability.
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