Money Laundering Detection

Money Laundering Detection

Shankar K P1, Vidya S 2
1 Student, Department of MCA, Bangalore Institute of Technology, Karnataka, India

2 Professor, Department of MCA, Bangalore Institute of Technology, Karnataka, India

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Abstract - Managing large amounts of data can be challenging in this era. Information Extraction and Ontology Learning are two vital technologies that address this difficulty. IE automatically retrieves and utilizes required data from unstructured data, using Named Entity Recognition techniques. OL is used to construct a formal representation of knowledge by organizing data into concepts and relationships between them based on the specific domain. These technologies enhance data organization, searchability, and interoperability, and when combined, they enable effective data analysis, tailored services, and the development of intelligent systems. They also play a vital role in advancing e-learning, as they retrieve and structure information related to the education sector, providing more organized and personalized efficient details for students and users. By leveraging IE and OL, educators can better align learning materials with student needs and track educational trends, improving the overall learning experience.

Key Words: Anti-Money Laundering (AML),Machine Learning (ML),Financial Fraud Detection,Random ForestReal-Time Prediction,Flask,Data Preprocessing