Social Aid Fraud Detection System and Poverty Map Model Suggestion Based on Data Mining for Social Risk Mitigation

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.

Data Mining ◽  
2013 ◽  
pp. 2230-2249
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.


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.


Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Massimiliano Zanin ◽  
Miguel Romance ◽  
Santiago Moral ◽  
Regino Criado

The detection of frauds in credit card transactions is a major topic in financial research, of profound economic implications. While this has hitherto been tackled through data analysis techniques, the resemblances between this and other problems, like the design of recommendation systems and of diagnostic/prognostic medical tools, suggest that a complex network approach may yield important benefits. In this paper we present a first hybrid data mining/complex network classification algorithm, able to detect illegal instances in a real card transaction data set. It is based on a recently proposed network reconstruction algorithm that allows creating representations of the deviation of one instance from a reference group. We show how the inclusion of features extracted from the network data representation improves the score obtained by a standard, neural network-based classification algorithm and additionally how this combined approach can outperform a commercial fraud detection system in specific operation niches. Beyond these specific results, this contribution represents a new example on how complex networks and data mining can be integrated as complementary tools, with the former providing a view to data beyond the capabilities of the latter.


2020 ◽  
Vol 214 ◽  
pp. 02042
Author(s):  
Shimin LEI ◽  
Ke XU ◽  
YiZhe HUANG ◽  
Xinye SHA

Credit card fraud leads to billions of losses in online transaction. Many corporations like Alibaba, Amazon and Paypal invest billions of dollars to build a safe transaction system. There are some studies in this area having tried to use machine learning or data mining to solve these problems. This paper proposed our fraud detection system for e- commerce merchant. Unlike many other works, this system combines manual and automatic classifications. This paper can inspire researchers and engineers to design and deploy online transaction systems.


2018 ◽  
pp. 286-312
Author(s):  
Masoumeh Zareapoor ◽  
Pourya Shamsolmoali ◽  
M. Afshar Alam

The fraud detection method requires a holistic approach where the objective is to correctly classify the transactions as legitimate or fraudulent. The existing methods give importance to detect all fraudulent transactions since it results in money loss. For this most of the time, they have to compromise on some genuine transactions. Thus, the major issue that the credit card fraud detection systems face today is that a significant percentage of transactions labelled as fraudulent are in fact legitimate. These “false alarms” delay the transactions and creates inconvenience and dissatisfaction to the customer. Thus, the objective of this research is to develop an intelligent data mining based fraud detection system for secure online payment transaction system. The performance evaluation of the proposed model is done on real credit card dataset and it is found that the proposed model has high fraud detection rate and less false alarm rate than other state-of-the-art classifiers.


2021 ◽  
Vol 11 (15) ◽  
pp. 6766
Author(s):  
Igor Mekterović ◽  
Mladen Karan ◽  
Damir Pintar ◽  
Ljiljana Brkić

Online shopping, already on a steady rise, was propelled even further with the advent of the COVID-19 pandemic. Of course, credit cards are a dominant way of doing business online. The credit card fraud detection problem has become relevant more than ever as the losses due to fraud accumulate. Most research on this topic takes an isolated, focused view of the problem, typically concentrating on tuning the data mining models. We noticed a significant gap between the academic research findings and the rightfully conservative businesses, which are careful when adopting new, especially black-box, models. In this paper, we took a broader perspective and considered this problem from both the academic and the business angle: we detected challenges in the fraud detection problem such as feature engineering and unbalanced datasets and distinguished between more and less lucrative areas to invest in when upgrading fraud detection systems. Our findings are based on the real-world data of CNP (card not present) fraud transactions, which are a dominant type of fraud transactions. Data were provided by our industrial partner, an international card-processing company. We tested different data mining models and approaches to the outlined challenges and compared them to their existing production systems to trace a cost-effective fraud detection system upgrade path.


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