Abstract
Purpose
Prior Big Data and fraud research emphasises technical detection or generic analytics, offering limited empirical insight into how Big Data is operationalised as practices and controls in e-retail fraud prevention. This study examines how e-retail firms translate analytics capabilities into fraud prevention practices and controls. Design/methodology/approach
The study draws on 32 semi-structured interviews across 18 e-retail organisations and applies abductive thematic analysis to examine how Big Data resources are translated into fraud prevention practices and controls.
Findings
Findings reveal that Big Data enables fraud prevention not merely through detection technologies, but through two empirically derived transformation practices: (1) Localised Exploitation, which translates complex analytics into simplified, role-specific reporting routines; and (2) Personalised Care Practice, which develops and cross-references business-unit-specific fraud risk profiles to identify emerging threats. These practices generate data-driven fraud controls by strengthening technical safeguards, refining formal policies, streamlining fraud reporting and reinforcing behavioural controls through analytics-informed training. Their effectiveness, however, is constrained by regulatory complexity, limited inter-organisational knowledge sharing and reliance on outsourced analytics providers.
Originality/value
This study makes three key empirical contributions. First, it moves beyond conceptual Big Data research by showing how analytics capabilities are enacted as organisational practices in e-retail fraud prevention. Second, it advances theory by integrating the Practice-Based View and the Balanced Control Paradigm into an empirically grounded Integrated Big Data-Enabled Fraud Prevention (IBDEFP) model. Third, it provides actionable insights into designing analytics, controls and human engagement to strengthen adaptive fraud management in volatile digital environments.
Prior Big Data and fraud research emphasises technical detection or generic analytics, offering limited empirical insight into how Big Data is operationalised as practices and controls in e-retail fraud prevention. This study examines how e-retail firms translate analytics capabilities into fraud prevention practices and controls. Design/methodology/approach
The study draws on 32 semi-structured interviews across 18 e-retail organisations and applies abductive thematic analysis to examine how Big Data resources are translated into fraud prevention practices and controls.
Findings
Findings reveal that Big Data enables fraud prevention not merely through detection technologies, but through two empirically derived transformation practices: (1) Localised Exploitation, which translates complex analytics into simplified, role-specific reporting routines; and (2) Personalised Care Practice, which develops and cross-references business-unit-specific fraud risk profiles to identify emerging threats. These practices generate data-driven fraud controls by strengthening technical safeguards, refining formal policies, streamlining fraud reporting and reinforcing behavioural controls through analytics-informed training. Their effectiveness, however, is constrained by regulatory complexity, limited inter-organisational knowledge sharing and reliance on outsourced analytics providers.
Originality/value
This study makes three key empirical contributions. First, it moves beyond conceptual Big Data research by showing how analytics capabilities are enacted as organisational practices in e-retail fraud prevention. Second, it advances theory by integrating the Practice-Based View and the Balanced Control Paradigm into an empirically grounded Integrated Big Data-Enabled Fraud Prevention (IBDEFP) model. Third, it provides actionable insights into designing analytics, controls and human engagement to strengthen adaptive fraud management in volatile digital environments.
| Original language | English |
|---|---|
| Number of pages | 31 |
| Journal | Journal of Enterprise Information Management |
| Early online date | 17 Apr 2026 |
| DOIs | |
| Publication status | E-pub ahead of print - 17 Apr 2026 |
Bibliographical note
Copyright © 2026 Emerald Publishing. This AAM is deposited under the CC BY-NC 4.0 licence. Any reuse is allowed in accordance with the terms outlined by the licence. To reuse the AAM for commercial purposes, permission should be sought by contacting [email protected]Keywords
- Big data
- Cyber Fraud Prevention Management
- E-Retail
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