47,479 research outputs found
Transfer learning approach for financial applications
Artificial neural networks learn how to solve new problems through a
computationally intense and time consuming process. One way to reduce the
amount of time required is to inject preexisting knowledge into the network. To
make use of past knowledge, we can take advantage of techniques that transfer
the knowledge learned from one task, and reuse it on another (sometimes
unrelated) task. In this paper we propose a novel selective breeding technique
that extends the transfer learning with behavioural genetics approach proposed
by Kohli, Magoulas and Thomas (2013), and evaluate its performance on financial
data. Numerical evidence demonstrates the credibility of the new approach. We
provide insights on the operation of transfer learning and highlight the
benefits of using behavioural principles and selective breeding when tackling a
set of diverse financial applications problems
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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