Intelligent visual analysis in employee fraud detection

Buket Doğan; Başar Öztayşi

doi:10.1007/978-3-030-23756-1_27

Intelligent visual analysis in employee fraud detection

Buket Doğan^*, Başar Öztayşi

^*Corresponding author for this work

Department of Industrial Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The objective of this paper is to discover and prevent organized financial crimes among various entities such as applicants, guarantors, customers, staff, portfolio managers, beneficiaries, agencies, experts and services. A sophisticated visual analysis solution can help users to detect conspicuous links between different entities. Most of the institutions have been using business intelligence or reporting tools to examine employee fraud which are insufficient to discover and analyze organized crime. In this paper, we propose a visual analytics approach to this problem. A web based application is developed which works with on-demand data and enable users to navigate on the graph and move objects in order to make the complex relationships clearer. The users can also zoom-in and zoom-out and dynamically expand nodes to discover hidden relationships at deeper levels. Intelligence Visual Analysis solution is a sub module of end-to-end Internal Fraud Management solution. It is also a stand-a-alone solution which can be integrated to other fraud management solutions.

Original language	English
Title of host publication	Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference
Editors	Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A.Cagri Tolga
Publisher	Springer Verlag
Pages	213-220
Number of pages	8
ISBN (Print)	9783030237554
DOIs	https://doi.org/10.1007/978-3-030-23756-1_27
Publication status	Published - 2020
Event	International Conference on Intelligent and Fuzzy Systems, INFUS 2019 - Istanbul, Turkey Duration: 23 Jul 2019 → 25 Jul 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1029
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	International Conference on Intelligent and Fuzzy Systems, INFUS 2019
Country/Territory	Turkey
City	Istanbul
Period	23/07/19 → 25/07/19

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

Employee fraud
Fraud
Link analysis
Misconduct
Organized crime
Visual analytics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-23756-1_27

Cite this

Doğan, B., & Öztayşi, B. (2020). Intelligent visual analysis in employee fraud detection. In C. Kahraman, S. Cevik Onar, B. Oztaysi, I. U. Sari, S. Cebi, & A. C. Tolga (Eds.), Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference (pp. 213-220). (Advances in Intelligent Systems and Computing; Vol. 1029). Springer Verlag. https://doi.org/10.1007/978-3-030-23756-1_27

Doğan, Buket ; Öztayşi, Başar. / Intelligent visual analysis in employee fraud detection. Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. editor / Cengiz Kahraman ; Sezi Cevik Onar ; Basar Oztaysi ; Irem Ucal Sari ; Selcuk Cebi ; A.Cagri Tolga. Springer Verlag, 2020. pp. 213-220 (Advances in Intelligent Systems and Computing).

@inproceedings{3429eb9d85d044b49978f4e377c02bc1,

title = "Intelligent visual analysis in employee fraud detection",

abstract = "The objective of this paper is to discover and prevent organized financial crimes among various entities such as applicants, guarantors, customers, staff, portfolio managers, beneficiaries, agencies, experts and services. A sophisticated visual analysis solution can help users to detect conspicuous links between different entities. Most of the institutions have been using business intelligence or reporting tools to examine employee fraud which are insufficient to discover and analyze organized crime. In this paper, we propose a visual analytics approach to this problem. A web based application is developed which works with on-demand data and enable users to navigate on the graph and move objects in order to make the complex relationships clearer. The users can also zoom-in and zoom-out and dynamically expand nodes to discover hidden relationships at deeper levels. Intelligence Visual Analysis solution is a sub module of end-to-end Internal Fraud Management solution. It is also a stand-a-alone solution which can be integrated to other fraud management solutions.",

keywords = "Employee fraud, Fraud, Link analysis, Misconduct, Organized crime, Visual analytics",

author = "Buket Doğan and Ba{\c s}ar {\"O}ztay{\c s}i",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; International Conference on Intelligent and Fuzzy Systems, INFUS 2019 ; Conference date: 23-07-2019 Through 25-07-2019",

year = "2020",

doi = "10.1007/978-3-030-23756-1_27",

language = "English",

isbn = "9783030237554",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

pages = "213--220",

editor = "Cengiz Kahraman and {Cevik Onar}, Sezi and Basar Oztaysi and Sari, {Irem Ucal} and Selcuk Cebi and A.Cagri Tolga",

booktitle = "Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference",

address = "Germany",

}

Doğan, B & Öztayşi, B 2020, Intelligent visual analysis in employee fraud detection. in C Kahraman, S Cevik Onar, B Oztaysi, IU Sari, S Cebi & AC Tolga (eds), Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. Advances in Intelligent Systems and Computing, vol. 1029, Springer Verlag, pp. 213-220, International Conference on Intelligent and Fuzzy Systems, INFUS 2019, Istanbul, Turkey, 23/07/19. https://doi.org/10.1007/978-3-030-23756-1_27

Intelligent visual analysis in employee fraud detection. / Doğan, Buket; Öztayşi, Başar.
Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. ed. / Cengiz Kahraman; Sezi Cevik Onar; Basar Oztaysi; Irem Ucal Sari; Selcuk Cebi; A.Cagri Tolga. Springer Verlag, 2020. p. 213-220 (Advances in Intelligent Systems and Computing; Vol. 1029).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Intelligent visual analysis in employee fraud detection

AU - Doğan, Buket

AU - Öztayşi, Başar

PY - 2020

Y1 - 2020

N2 - The objective of this paper is to discover and prevent organized financial crimes among various entities such as applicants, guarantors, customers, staff, portfolio managers, beneficiaries, agencies, experts and services. A sophisticated visual analysis solution can help users to detect conspicuous links between different entities. Most of the institutions have been using business intelligence or reporting tools to examine employee fraud which are insufficient to discover and analyze organized crime. In this paper, we propose a visual analytics approach to this problem. A web based application is developed which works with on-demand data and enable users to navigate on the graph and move objects in order to make the complex relationships clearer. The users can also zoom-in and zoom-out and dynamically expand nodes to discover hidden relationships at deeper levels. Intelligence Visual Analysis solution is a sub module of end-to-end Internal Fraud Management solution. It is also a stand-a-alone solution which can be integrated to other fraud management solutions.

AB - The objective of this paper is to discover and prevent organized financial crimes among various entities such as applicants, guarantors, customers, staff, portfolio managers, beneficiaries, agencies, experts and services. A sophisticated visual analysis solution can help users to detect conspicuous links between different entities. Most of the institutions have been using business intelligence or reporting tools to examine employee fraud which are insufficient to discover and analyze organized crime. In this paper, we propose a visual analytics approach to this problem. A web based application is developed which works with on-demand data and enable users to navigate on the graph and move objects in order to make the complex relationships clearer. The users can also zoom-in and zoom-out and dynamically expand nodes to discover hidden relationships at deeper levels. Intelligence Visual Analysis solution is a sub module of end-to-end Internal Fraud Management solution. It is also a stand-a-alone solution which can be integrated to other fraud management solutions.

KW - Employee fraud

KW - Fraud

KW - Link analysis

KW - Misconduct

KW - Organized crime

KW - Visual analytics

UR - http://www.scopus.com/inward/record.url?scp=85069478257&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-23756-1_27

DO - 10.1007/978-3-030-23756-1_27

M3 - Conference contribution

AN - SCOPUS:85069478257

SN - 9783030237554

T3 - Advances in Intelligent Systems and Computing

SP - 213

EP - 220

BT - Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference

A2 - Kahraman, Cengiz

A2 - Cevik Onar, Sezi

A2 - Oztaysi, Basar

A2 - Sari, Irem Ucal

A2 - Cebi, Selcuk

A2 - Tolga, A.Cagri

PB - Springer Verlag

T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2019

Y2 - 23 July 2019 through 25 July 2019

ER -

Doğan B, Öztayşi B. Intelligent visual analysis in employee fraud detection. In Kahraman C, Cevik Onar S, Oztaysi B, Sari IU, Cebi S, Tolga AC, editors, Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. Springer Verlag. 2020. p. 213-220. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-23756-1_27

Intelligent visual analysis in employee fraud detection

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this