Published Paper Details:

Financial fraud poses a significant threat to business and individual alike Detecting fraudulent transactions in financial data is crucial to safeguarding asset and maintaining trust in financial systems. The project explores the application of One-Class SVM as an effective and efficient tool for identifying fraudulent transactions within large datasets. Financial institutions, payment processors and e-commerce platforms commonly employ such fraud detection techniques to safeguard against financial losses and maintain trust among their customers. The use of One-Class SVM is particularly valuable in situations where fraudulent cases are rare and difficult to distinguish from normal transactions, using traditional classification techniques.-- Financial fraud poses a significant threat to business and individual alike Detecting fraudulent transactions in financial data is crucial to safeguarding asset and maintaining trust in financial systems. The project explores the application of One-Class SVM as an effective and efficient tool for identifying fraudulent transactions within large datasets. Financial institutions, payment processors and e-commerce platforms commonly employ such fraud detection techniques to safeguard against financial losses and maintain trust among their customers. The use of One-Class SVM is particularly -- Financial fraud poses a significant threat to business and individual alike Detecting fraudulent transactions in financial data is crucial to safeguarding asset and maintaining trust in financial systems. The project explores the application of One-Class SVM as an effective and efficient tool for identifying fraudulent transactions within large datasets. Financial institutions, payment processors and e-commerce platforms commonly employ such fraud detection techniques to safeguard against financial losses and maintain trust among their customers. The use of One-Class SVM is particularly valuable in situations where fraudulent cases are rare and difficult to distinguish from normal transactions, using traditional classification techniques.in situations where fraudulent cases are rare and difficult to distinguish from normal transactions, using traditional classification techniques.