Credit Card Fraud Detection Using Symbolic Aggregate Approximation

Document Type

Conference Proceeding

Source of Publication

2024 6th International Conference on Blockchain Computing and Applications (BCCA)

Publication Date

11-29-2024

Abstract

In the current digital age, credit card fraud is a cybercrime that costs billions of dollars every year through fraudulent transactions. This study aims to identify fraudulent credit card transactions using Symbolic Aggregate Approximation (SAX) and anomaly discovery through motifs and discords. To do this, credit card data is used as time series data and then divided into equal-length segments. Each segment is then converted into a symbolic representation using a pre-defined threshold. A web-based interface is further developed to load a collection of credit card transaction data, based on the proposed SAX method. The preliminary results show that the proposed approach is capable of identifying fraudulent transactions within a set of transactions. By integrating this approach with other fraud detection mechanisms, a near real-time fraud detection process can be developed. This approach therefore provides a potential for context-independent fraud detection which is a major challenge in the fraud detection domain.

ISBN

979-8-3503-5153-8

Publisher

IEEE

Volume

00

First Page

441

Last Page

448

Disciplines

Computer Sciences

Keywords

Credit card fraud, Symbolic Aggregate Approximation, Anomaly detection, Time series data, Fraud detection mechanisms

Indexed in Scopus

no

Open Access

no

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