An Empirical Assessment of Integrated Fraud Detection Mechanisms:Evidence from Finance and Audit Professionals

Abstract

This study examines the effectiveness of an integrated fraud detection framework combining data‑driven forensic analytics, internal control systems, and continuous auditing mechanisms. While prior research often examines these components in isolation, this study adopts a unified empirical approach to assess their joint contribution to perceived fraud detection effectiveness. Design/methodology/approach: A quantitative cross‑sectional design is employed using primary survey data collected from 120 finance, audit, and risk professionals across banking, manufacturing, public sector, and professional services in Nigeria. Multi‑item Likert scales measure each construct. Data are analyzed using reliability testing, Pearson correlation, and hierarchical multiple regression with control variables (experience, sector, organization size). Common method bias is addressed through procedural remedies (anonymity, no right/wrong answers, separated questionnaire sections) and assessed via Harman’s single‑factor test. Post‑hoc power analysis confirms adequate sample size. Validity is supported through confirmatory factor analysis. Findings: All three components have positive and statistically significant effects on perceived fraud detection effectiveness. Data‑driven forensic analytics is the strongest predictor (β = 0.41, p < 0.001), followed by internal control systems (β = 0.36, p < 0.01) and continuous auditing (β = 0.30, p < 0.01). The model explains 65% of the variance (adjusted R² = 0.64). Interaction tests reveal that analytics and continuous auditing complement each other, supporting the integrated architecture claim. Research limitations/implications: The study uses perception‑based data and a cross‑sectional design, limiting causal inference. Common method bias is tested and found not to be a substantial threat, but objective outcome data (e.g., actual detected fraud cases) would strengthen future research. The theoretical framework integrates Fraud Triangle Theory, Agency Theory, and the Information Systems Success Model, demonstrating how technology‑enabled monitoring reduces opportunity and information asymmetry. Practical implications: Organizations seeking to enhance fraud detection should prioritize investment in data analytics capabilities, reinforce internal controls, and implement continuous auditing as a complementary system. The integrated architecture yields greater effectiveness than isolated implementations. Originality/value : This study provides empirical evidence from an emerging economy context on the complementary roles of analytics, governance, and real‑time monitoring within a single empirical model – an integrated framework rarely tested simultaneously in prior literature.