The University of Southampton
University of Southampton Institutional Repository

Using big data analytics to combat retail fraud

Using big data analytics to combat retail fraud
Using big data analytics to combat retail fraud
Fraudulent returns are seen as a misfortune for most retailers because it reduces sales and induce greater costs and challenges in returns management. While extant research suggests one of the causes is retailers’ liberal return policies and that retailers should restrict their policies, there is no study systematically exploring the impacts of various return policies and fraud interventions on reducing different types of fraudulent behaviour and the costs and benefits of associated interventions. In this paper, we first undertook semi-structured interviews with retailers in the UK and North America to gain insights into their fraud intervention strategies, as well as conducted literature review on fraudulent returns to identify the influential factors that lead customers to return products fraudulently. On this basis, we developed a simulation model to help retailers forecast fraudulent returns and explore how different combinations of interventions might affect the cases of fraudulent returns and associated financial impacts on profitability. The background literature on fraudulent returns, the findings of interviews, and the demonstration and implications of the model on reducing fraudulent returns and related financial impacts are discussed. Our model allows retailers to make cost- effective evaluations and adopt their fraud prevention strategies effectively based on their business models.
Fraudulent Returns, Simulation, Returns Policy, Fraud Interventions, Retail Strategic Management
85-92
Zhang, Danni
c81a5801-9c21-4c27-a340-45874b5274f9
Bayer, Steffen
28979328-d6fa-4eb7-b6de-9ef97f8e8e97
Wills, Gary
3a594558-6921-4e82-8098-38cd8d4e8aa0
Frei, Regina
fa00170f-356a-4a0d-8030-d137fd855880
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Senyo, PK
b2150f66-8ef9-48f7-af32-3b055d4fa691
Zhang, Danni
c81a5801-9c21-4c27-a340-45874b5274f9
Bayer, Steffen
28979328-d6fa-4eb7-b6de-9ef97f8e8e97
Wills, Gary
3a594558-6921-4e82-8098-38cd8d4e8aa0
Frei, Regina
fa00170f-356a-4a0d-8030-d137fd855880
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Senyo, PK
b2150f66-8ef9-48f7-af32-3b055d4fa691

Zhang, Danni, Bayer, Steffen, Wills, Gary, Frei, Regina, Gerding, Enrico and Senyo, PK (2022) Using big data analytics to combat retail fraud. the 4th International Conference on Finance, Economics, Management and IT Business. pp. 85-92 . (doi:10.5220/0011042600003206).

Record type: Conference or Workshop Item (Paper)

Abstract

Fraudulent returns are seen as a misfortune for most retailers because it reduces sales and induce greater costs and challenges in returns management. While extant research suggests one of the causes is retailers’ liberal return policies and that retailers should restrict their policies, there is no study systematically exploring the impacts of various return policies and fraud interventions on reducing different types of fraudulent behaviour and the costs and benefits of associated interventions. In this paper, we first undertook semi-structured interviews with retailers in the UK and North America to gain insights into their fraud intervention strategies, as well as conducted literature review on fraudulent returns to identify the influential factors that lead customers to return products fraudulently. On this basis, we developed a simulation model to help retailers forecast fraudulent returns and explore how different combinations of interventions might affect the cases of fraudulent returns and associated financial impacts on profitability. The background literature on fraudulent returns, the findings of interviews, and the demonstration and implications of the model on reducing fraudulent returns and related financial impacts are discussed. Our model allows retailers to make cost- effective evaluations and adopt their fraud prevention strategies effectively based on their business models.

This record has no associated files available for download.

More information

Accepted/In Press date: 25 April 2022
Published date: 25 April 2022
Venue - Dates: the 4th International Conference on Finance, Economics, Management and IT Business, 2022-04-24
Keywords: Fraudulent Returns, Simulation, Returns Policy, Fraud Interventions, Retail Strategic Management

Identifiers

Local EPrints ID: 457311
URI: http://eprints.soton.ac.uk/id/eprint/457311
PURE UUID: bfafc8bd-f582-4971-a829-59ca93de2933
ORCID for Danni Zhang: ORCID iD orcid.org/0000-0002-2729-9562
ORCID for Steffen Bayer: ORCID iD orcid.org/0000-0002-7872-467X
ORCID for Gary Wills: ORCID iD orcid.org/0000-0001-5771-4088
ORCID for Regina Frei: ORCID iD orcid.org/0000-0002-0953-6413
ORCID for Enrico Gerding: ORCID iD orcid.org/0000-0001-7200-552X
ORCID for PK Senyo: ORCID iD orcid.org/0000-0001-7126-3826

Catalogue record

Date deposited: 01 Jun 2022 16:30
Last modified: 17 Mar 2024 04:06

Export record

Altmetrics

Contributors

Author: Danni Zhang ORCID iD
Author: Steffen Bayer ORCID iD
Author: Gary Wills ORCID iD
Author: Regina Frei ORCID iD
Author: Enrico Gerding ORCID iD
Author: PK Senyo ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×