Data mining techniques in financial fraud detection

Seminar Paper , 2016 , 12 Pages , Grade: 1.7

Computer Science - General

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.

Contents

List of Abbreviations

List of tables

1. Introduction
1.1. Goals
1.2. Structure of seminar thesis

2. Terminology
2.1. Data Mining
2.2. Fraud
2.3. Financial Fraud
2.4. Insurance Fraud
2.5. Bank Fraud

3. Research methodology

4. Classification of Data Mining Applications

5. Literature Review

6. Conclusion

References

Print sources

Internet sources

• College: Heilbronn University
• Grade: 1.7
• Author: Rohan Ahmed (Author)
• Publication Year: 2016
• Pages: 12
• Catalog Number: V426829
• ISBN (eBook): 9783668709270
• ISBN (Book): 9783668709287
• Language: English
• Tags: Fraud, Fraud Detection, Literature review, Data Mining techniques, financial fraud detection, Data mining techniques in financial fraud detection, Financial Fraud, Insurance Fraud, Bank Fraud, Classification of Data Mining Applications, Support Vector Machine, Logistic Regression, DM, Decision Tree