Analysis of Scope of Data Mining in Fraud Detection and Studying Solutions to Prevent Them

Author(s):  
Nouby Mahdy Ghazaly

This paper will explain the process of fraud detection suing datamining techniques. Fraud detection is important task and many domains have risk of attack of fraudsters in the data that they have stored. It is very important that each domain like banking etc should have reliable fraud detection scheme so that the personal details of the users of the banks is safe and secure. There are a lot of techniques which can be used to detect fraud attack in the system

2019 ◽  
Vol 3 (2) ◽  
pp. 10
Author(s):  
Ardalan Husin Awlla

In this period of computerization, schooling has additionally remodeled itself and is not restrained to old lecture technique. The everyday quest is on to discover better approaches to make it more successful and productive for students. These days, masses of data are gathered in educational databases, however it stays unutilized. To be able to get required advantages from such major information, effective tools are required. Data mining is a developing capable tool for examination and expectation. It is effectively applied in the field of fraud detection, marketing, promoting, forecast and loan assessment. However, it is in incipient stage in the area of education. In this paper, data mining techniques have been applied to construct a classification model to predict the performance of students.


2017 ◽  
Vol 20 (3) ◽  
pp. 301-310 ◽  
Author(s):  
Noriaki Yasaka

Purpose This report aims to focus on how suspicious transaction report is created with data mining methods and used from the point of view of knowledge management. Design/methodology/approach This paper considers data mining versus knowledge management in the anti-money laundering (AML) field. Findings In the AML field, the information and knowledge gained are not necessarily used for or shared with the related shareholders. Creating and co-evolving the network of “knowledge professionals” is the impending assignment in this industry. The first and most important task is knowledge management in the global AML field. Originality/value The report considers the creation with data mining methods and utilization from the point of view of knowledge management.


Web Services ◽  
2019 ◽  
pp. 618-638
Author(s):  
Goran Klepac ◽  
Kristi L. Berg

This chapter proposes a new analytical approach that consolidates the traditional analytical approach for solving problems such as churn detection, fraud detection, building predictive models, segmentation modeling with data sources, and analytical techniques from the big data area. Presented are solutions offering a structured approach for the integration of different concepts into one, which helps analysts as well as managers to use potentials from different areas in a systematic way. By using this concept, companies have the opportunity to introduce big data potential in everyday data mining projects. As is visible from the chapter, neglecting big data potentials results often with incomplete analytical results, which imply incomplete information for business decisions and can imply bad business decisions. The chapter also provides suggestions on how to recognize useful data sources from the big data area and how to analyze them along with traditional data sources for achieving more qualitative information for business decisions.


Author(s):  
Roberto Marmo

As a conseguence of expansion of modern technology, the number and scenario of fraud are increasing dramatically. Therefore, the reputation blemish and losses caused are primary motivations for technologies and methodologies for fraud detection that have been applied successfully in some economic activities. The detection involves monitoring the behavior of users based on huge data sets such as the logged data and user behavior. The aim of this contribution is to show some data mining techniques for fraud detection and prevention with applications in credit card and telecommunications, within a business of mining the data to achieve higher cost savings, and also in the interests of determining potential legal evidence. The problem is very difficult because fraudsters takes many different forms and are adaptive, so they will usually look for ways to avoid every security measures.


Author(s):  
Ali Serhan Koyuncugil ◽  
Nermin Ozgulbas

After last global financial crisis, one of the most important concerns of the governments became unemployment. Higher unemployment rates haves been forcing governments to develop some policies. Some of these policies has been included financial policies while some of them included social policies. One of the most important concerns of social policies is social risk mitigation and fight against poverty and social aids as its extensions. In general, measurement of social events have been mostly based on subjective statements. More specifically, targeting mechanisms have been using for determination of potential social aid owners. Most popular targeting mechanisms are subjective ones as well. In this chapter, an objective targeting mechanism model and a fraud detection system model have been developed via data mining for social aids as an identifier of poverty levels which includes early warning signals for inappropriate applications. Then, these models have been used for development of a poverty map. Developed new targeting mechanism which has been based on rating approach will be an alternative to Means Test and Proxy Means Test. In addition, social aid fraud detection system will be updated automatic with Intelligent System property and the poverty map computation approach can be used for absence of detailed data. Furthermore, Millenium Development Goals, Targeting Mechanisms, Poverty and Poverty Maps concepts have been reviewed from an analytical and objective point of view.


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