Data mining techniques in financial fraud detection

Rohan Ahmed

ISBN 10: 3668709289 / ISBN 13: 9783668709287

Published by GRIN Verlag, GRIN Verlag Jun 2018, 2018

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -Seminar paper from the year 2016 in the subject Computer Science - General, grade: 1.7, Heilbronn University, language: English, abstract: In this seminar thesis you will get a view about the Data Mining techniques in financial fraud detection. Financial Fraud is taking a big issue in economical problem, which is still growing. So there is a big interest to detect fraud, but by large amounts of data, this is difficult. Therefore, many data mining techniques are repeatedly used to detect frauds in fraudulent activities. Majority of fraud area are Insurance, Banking, Health and Financial Statement Fraud. The most widely used data mining techniques are Support Vector Machines (SVM), Decision Trees (DT), Logistic Regression (LR), Naives Bayes, Bayesian Belief Network, Classification and Regression Tree (CART) etc. These techniques existed for many years and are used repeatedly to develop a fraud detection system or for analyze frauds.Books on Demand GmbH, Überseering 33, 22297 Hamburg 20 pp. Englisch.

Seller Inventory # 9783668709287

Bibliographic Details

• Title: Data mining techniques in financial fraud detection
• Publisher: GRIN Verlag, GRIN Verlag Jun 2018
• Publication Date: 2018
• Language: English
• ISBN 10: 3668709289
• ISBN 13: 9783668709287
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

Seminar paper from the year 2016 in the subject Computer Science - General, grade: 1.7, Heilbronn University, language: English, abstract: In this seminar thesis you will get a view about the Data Mining techniques in financial fraud detection. Financial Fraud is taking a big issue in economical problem, which is still growing. So there is a big interest to detect fraud, but by large amounts of data, this is difficult. Therefore, many data mining techniques are repeatedly used to detect frauds in fraudulent activities. Majority of fraud area are Insurance, Banking, Health and Financial Statement Fraud. The most widely used data mining techniques are Support Vector Machines (SVM), Decision Trees (DT), Logistic Regression (LR), Naives Bayes, Bayesian Belief Network, Classification and Regression Tree (CART) etc. These techniques existed for many years and are used repeatedly to develop a fraud detection system or for analyze frauds.