A Deep Learning Approach to Fraud Detection in Financial Payment Services

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Seddighi Amir Hossein; Sajedinejad Arman

Educational Videos and Scientific Workshops

Scientific Information Database

ISSN: 2588-4824

Journal Paper

Paper Information

Journal: IRANIAN JOURNAL OF INFORMATION MANAGEMENT Year:2019 | Volume:5 | Issue:1 (8) Page(s): 166-182

Download Full-Text

Persian Verion

View:

764

Download:

Cites:

Information Journal Paper

Title

A Deep Learning Approach to Fraud Detection in Financial Payment Services

Author(s)

Seddighi Amir Hossein | Sajedinejad Arman | Issue Writer Certificate

Keywords

Artificial IntelligenceQ3

Deep LearningQ1

Fraud DetectionQ1

Information TechnologyQ1

Machine LearningQ1

Abstract

Widespread use of financial payment services besides the increased volume of financial transfers carried out in banking systems, have resulted in an important growing trend of automatic Fraud Detection. In this regard, an intelligent system is needed that can determine the fraudulence or genuineness of a financial transaction in real-time, unquestionably with an acceptable precision using the different transaction features. In order to gain the benefits of the system, a Deep Learning algorithm is described and proposed in this paper. The performance of the proposed algorithm is evaluated using a set of real-world financial transactions, which is known as the standard dataset in the literature. Then, the proposed algorithm is compared with k-nearest neighbors and support vector machine algorithms using different metrics such as accuracy, precision, F-measure, sensitivity, and precision-recall curve. The computational results confirmed the efficiency of the proposed algorithm on the standard dataset with 96% accuracy and 98% precision.

Cites

No record.

References

No record.

Cite

APA: Copy

Seddighi, Amir Hossein, & Sajedinejad, Arman. (2019). A Deep Learning Approach to Fraud Detection in Financial Payment Services. IRANIAN JOURNAL OF INFORMATION MANAGEMENT, 5(1 (8) ), 166-182. SID. https://sid.ir/paper/377963/en

Vancouver: Copy

Seddighi Amir Hossein, Sajedinejad Arman. A Deep Learning Approach to Fraud Detection in Financial Payment Services. IRANIAN JOURNAL OF INFORMATION MANAGEMENT[Internet]. 2019;5(1 (8) ):166-182. Available from: https://sid.ir/paper/377963/en

IEEE: Copy

Amir Hossein Seddighi, and Arman Sajedinejad, “A Deep Learning Approach to Fraud Detection in Financial Payment Services,” IRANIAN JOURNAL OF INFORMATION MANAGEMENT, vol. 5, no. 1 (8) , pp. 166–182, 2019, [Online]. Available: https://sid.ir/paper/377963/en

Related Journal Papers

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops

Download

Move to top

وبگردی