Research of Conventional Data Mining Tools for Big Data Handling in Finance Institutions

Tamasauskas, Darius; Liutvinavicius, Marius; Sakalauskas, Virgilijus; Kriksciuniene, Dalia

doi:10.1007/978-3-642-41687-3_5

Cited by 3 publications

(1 citation statement)

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“…Advanced data mining techniques and improved models of risk management could help to solve the problem of evaluating information adequately and discovering potential threats [9]. According to Han and Harcourt [6], data mining is extracting knowledge from large amounts of data, which is recently nominated as "big data" problem.…”

Section: Introductionmentioning

confidence: 99%

Research of customer behavior anomalies in big financial data

Kriksciuniene

Liutvinavicius

Sakalauskas

et al. 2014

2014 14th International Conference on Hybrid Intelligent Systems

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The amount of data in financial institutions is growing rapidly and the subject of "big data" has become an urgent trend. The "big data" phenomenon brings challenge to empower analytical methods for enhanced scope. At the same time the big data composed from various sources opens new possibilities to capitalize data research. The article investigates the anomalies in big data used by financial institutions. It proposes the model designed for exploring dynamics and detecting anomalous behavior of bank customers. The experimental screening on bank customers' big data shows significant time and necessary calculation steps reduction for detecting suspicious operations.

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Section: Introductionmentioning

confidence: 99%