Abstract
As of now, enormous electronic information stores are being kept up by banks and other money-related organizations. Information mining advancement gives the region to get to the right information at the right time from massive volumes of information. Data classification is an established issue in AI and information mining. In regular choice (decision) tree investigation, a normal for a tuple is either supreme or partial. The choice tree calculations are utilized for dissecting solid and numerical information of uses. In the surviving techniques, they play out the extended model of choice tree examination to help information tuple having factual characteristics with uncertainty characterized by discretionary pdf. Along these lines, we proposed an improved novel choice tree for the two information, speaking to the development and a framework utilized for AI and information mining to strengthen the requirement of the financial undertaking. This paper expects to evaluate the utilization of strategies for choice trees to aid the trepidation of bank extortion. The choice trees aid this work of choosing the characteristic that will build up a superior exhibition in determining the odds of bank fraud.
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Khare, N., Viswanathan, P. (2020). Decision Tree-Based Fraud Detection Mechanism by Analyzing Uncertain Data in Banking System. In: Venkata Krishna, P., Obaidat, M. (eds) Emerging Research in Data Engineering Systems and Computer Communications. Advances in Intelligent Systems and Computing, vol 1054. Springer, Singapore. https://doi.org/10.1007/978-981-15-0135-7_8
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DOI: https://doi.org/10.1007/978-981-15-0135-7_8
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