6,746 research outputs found
Neural data mining for credit card fraud detection
The prevention of credit card fraud is an important application for prediction techniques. One major obstacle for using neural network training techniques is the high necessary diagnostic quality: Since only one financial transaction of a thousand is invalid no prediction success less than 99.9% is acceptable. Due to these credit card transaction proportions complete new concepts had to be developed and tested on real credit card data. This paper shows how advanced data mining techniques and neural network algorithm can be combined successfully to obtain a high fraud coverage combined with a low false alarm rate
Mining Bad Credit Card Accounts from OLAP and OLTP
Credit card companies classify accounts as a good or bad based on historical
data where a bad account may default on payments in the near future. If an
account is classified as a bad account, then further action can be taken to
investigate the actual nature of the account and take preventive actions. In
addition, marking an account as "good" when it is actually bad, could lead to
loss of revenue - and marking an account as "bad" when it is actually good,
could lead to loss of business. However, detecting bad credit card accounts in
real time from Online Transaction Processing (OLTP) data is challenging due to
the volume of data needed to be processed to compute the risk factor. We
propose an approach which precomputes and maintains the risk probability of an
account based on historical transactions data from offline data or data from a
data warehouse. Furthermore, using the most recent OLTP transactional data,
risk probability is calculated for the latest transaction and combined with the
previously computed risk probability from the data warehouse. If accumulated
risk probability crosses a predefined threshold, then the account is treated as
a bad account and is flagged for manual verification.Comment: Conference proceedings of ICCDA, 201
Credit card fraud detection by adaptive neural data mining
The prevention of credit card fraud is an important application for prediction techniques. One major obstacle for using neural network training techniques is the high necessary diagnostic quality: Since only one financial transaction of a thousand is invalid no prediction success less than 99.9% is acceptable. Due to these credit card transaction proportions complete new concepts had to be developed and tested on real credit card data. This paper shows how advanced data mining techniques and neural network algorithm can be combined successfully to obtain a high fraud coverage combined with a low false alarm rate
Electronic fraud detection in the U.S. Medicaid Healthcare Program: lessons learned from other industries
It is estimated that between 850 billion annually is lost to fraud, waste, and abuse in the US healthcare system,with 175 billion of this due to fraudulent activity (Kelley 2009). Medicaid, a state-run, federally-matchedgovernment program which accounts for roughly one-quarter of all healthcare expenses in the US, has been particularlysusceptible targets for fraud in recent years. With escalating overall healthcare costs, payers, especially government-runprograms, must seek savings throughout the system to maintain reasonable quality of care standards. As such, the need foreffective fraud detection and prevention is critical. Electronic fraud detection systems are widely used in the insurance,telecommunications, and financial sectors. What lessons can be learned from these efforts and applied to improve frauddetection in the Medicaid health care program? In this paper, we conduct a systematic literature study to analyze theapplicability of existing electronic fraud detection techniques in similar industries to the US Medicaid program
Intelligent Financial Fraud Detection Practices: An Investigation
Financial fraud is an issue with far reaching consequences in the finance
industry, government, corporate sectors, and for ordinary consumers. Increasing
dependence on new technologies such as cloud and mobile computing in recent
years has compounded the problem. Traditional methods of detection involve
extensive use of auditing, where a trained individual manually observes reports
or transactions in an attempt to discover fraudulent behaviour. This method is
not only time consuming, expensive and inaccurate, but in the age of big data
it is also impractical. Not surprisingly, financial institutions have turned to
automated processes using statistical and computational methods. This paper
presents a comprehensive investigation on financial fraud detection practices
using such data mining methods, with a particular focus on computational
intelligence-based techniques. Classification of the practices based on key
aspects such as detection algorithm used, fraud type investigated, and success
rate have been covered. Issues and challenges associated with the current
practices and potential future direction of research have also been identified.Comment: Proceedings of the 10th International Conference on Security and
Privacy in Communication Networks (SecureComm 2014
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