Adoption of AI-Driven Fraud Detection System in the Nigerian Banking Sector: An Analysis of Cost, Compliance, and Competency

TL;DR

This study investigates the factors influencing the adoption of AI-driven fraud detection systems in Nigerian banks, highlighting the roles of management support, infrastructure, compliance, staff skills, and costs in adoption decisions.

Contribution

It identifies key determinants affecting AI fraud detection adoption in Nigeria and offers practical recommendations for banks to enhance implementation and integration.

Findings

Top management support and IT infrastructure promote adoption.

High implementation costs hinder AI system uptake.

Regulatory compliance and staff competency facilitate adoption.

Abstract

The inception of AI-based fraud detection systems has presented the banking sector across the globe the opportunity to enhance fraud prevention mechanisms. However, the extent of adoption in Nigeria has been slow, fragmented, and inconsistent due to high cost of implementation and lack of technical expertise. 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 banks 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 it. Therefore, the study recommended that banks 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; embrace continuous professional development in AI, and fraud analytics for IT, fraud investigation, and risk management staff.