Adoption of Forensic Accounting Techniques and Contemporary Fraud Detection Mechanisms in the Financial Sector in Zimbabwe

Zimbabwe's financial sector faces a sophisticated wave of financial crime that outpaces traditional compliance-driven, paper-based oversight. This study examines the incorporation of forensic accounting techniques in registered commercial banks and insurance companies in Harare, investigating the gap between formal adoption and practical, real-time day-to-day application. Grounded in Fraud Diamond Theory, Differential Association Theory, and Institutional Theory, the study explains individual drivers of fraud alongside institutional performance dynamics. A sequential explanatory mixed-methods design was employed. Quantitative questionnaires were gathered from 100 purposively selected oversight professionals and analyzed using SPSS, followed by ten semi-structured thematic interviews analyzed via NVivo. Findings show an industry formally compliant but operationally under-equipped: 92% of institutions remain reliant on compliance checklists, 64% report no predictive analytics, and 72% lack continuous digital surveillance. However, data mining adoption strongly correlates with fraud detection effectiveness (Pearson r = 0.742, p < .01), and machine learning accounts for 61% of financial loss reduction variance. Structural barriers include legacy system incompatibility (68%), prohibitively expensive foreign licensing costs, and an entrenched tick-box culture (82%). A decoupling effect exists where compliance satisfies regulatory mandates on paper while departments remain operationally under-resourced. Compulsory forensic readiness audits and API-integrated systems are recommended.