Fixed-width confidence interval for two-stage response-adaptive designs in ridit analysis

Bandyopadhyay, Uttam; Biswas, Atanu

doi:10.1016/j.spl.2015.09.020

Cited by 2 publications

(1 citation statement)

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“…There are many predictive methods namely supervised method to detect fraud such as support vector machine [6], neural network [7], [8] and decision tree [9], [10]. While, an unsupervised method, a few techniques have been applied such as Selforganizing Map [11], [12], Principal Component Analysis [13], [14] and Peer Group Analysis [15]. However, most of these methods are dependent on distribution which is not appropriate for ordered categorical data.…”

Section: Introductionmentioning

confidence: 99%

Credit Card Detection System Based on Ridit Approach

Tukiman¹,

Ahmad²,

Saad³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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Fraud detection is one of the important agendas in financial and insurance institutions to protect the institutions from fraudsters and loss. The losses to the financial institutions are huge, and the need to detect the fraud at an early stage is critical to the institutions. If the numbers of fraud are not properly managed, the impact may lead to the closure of the institutions. Many predictive analytic systems or models have been proposed to identify and detect the frauds. Hence, this paper examines the effect of different response variables of credit card history as the reference group which used an unsupervised scoring method namely an Identified Distribution (RIDIT) based on a statistically significant test. We illustrate the method using German Credit card dataset retrieved from UCI Machine Learning Data System. The result generates scores and significant value of chi-square test that reflect response variables being classified as reference group or comparison groups, which more or less affected by the response credit card history in fraud detection.

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