Adoption of AI-Driven Fraud Detection System in the Nigerian Banking Sector: An Analysis of Cost, Compliance, and Competency
Year: 2025
Type: article
Study design: Observational Study
Abstract: The inception of AI-based fraud detection systems has offered the banking sector across the globe opportunities to improve fraud prevention mechanisms. However, the extent of AI-driven fraud detection adoption in the Nigerian banking sector has been slow, fragmented, and inconsistent due to high cost of implementation, lack of technical expertise, infrastructure challenges and regulatory uncertainty surrounding data privacy and cybersecurity. This study seeks to investigate extent of adoption and determinants of AI-driven fraud detection systems in Nigerian banks. This study adopted a cross-sectional survey research design. Data were extracted from primary sources through structured questionnaire based on 5-point Likert scale. The population of the study consist of 24 licensed deposit money banks (DMBs) in Nigeria. A purposive sampling technique was used to select 5 biggest banks based on market capitalization and customer base. The Ordered Logistic Regression (OLR) model was used to estimate the data. The results showed that top management support, IT infrastructure, regulatory compliance, staff competency and perceived effectiveness accelerate the uptake of AI-driven fraud detection systems adoption. However, high implementation cost discourages banks from AI-driven fraud detection systems adoption. Therefore, the study recommended that DMBs should invest in modern and scalable IT systems that support the integration of AI tools; adopt open-source or cloud-based AI platforms that are cost-effective and scalable; embrace continuous professional development in AI, machine learning, and fraud analytics for IT, fraud investigation, and risk management staff.
Source: Financial Economics Letters
DOI: 10.58567/fel04020004
Authors: John Stephen Alaba, Shonubi Joye Ahmed, Azuikpe Patience Farida, Ologun Victor Oluwatosin
Institutions: Kwara State University, University of Manchester, Le Moyne College
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4416062333