| Data mining techniques in financial fraud detection Contributor(s): Ahmed, Rohan (Author) |
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ISBN: 3668709289 ISBN-13: 9783668709287 Publisher: Grin Verlag OUR PRICE: $36.01 Product Type: Paperback Published: June 2018 |
| Additional Information |
| BISAC Categories: - Computers | Programming Languages - General - Computers | Databases - Data Mining |
| Physical Information: 0.04" H x 7" W x 10" (0.12 lbs) 20 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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. |