Using the Technology Acceptance Model in Assessing the Impact of Financial Intelligence Systems on Money Laundering Detection in Zimbabwean Financial Institutions

Abstract

Money laundering posed significant global threats, prompting urgent reforms and technological advancements. In this context, a study was conducted to investigate the acceptance of financial intelligence systems (FIS) among employees in Zimbabwean financial institutions. Utilising a quantitative, cross-sectional survey design based on the Technology Acceptance Model (TAM), researchers collected data from 289 employees representing banks (52%), microfinance institutions (33%) and insurance firms (15%), achieving an impressive response rate of 82.57%. The demographic analysis revealed a diverse sample, with 58.82% of respondents identifying as male and 41.18% as female, the majority being aged 31-40 years (41.52%). Key findings indicated that Perceived Usefulness (PU) emerged as the strongest predictor of Attitude Towards Using (ATU) FIS, with a statistical coefficient of β = 0.60 (p < 0.001), while Perceived Ease of Use (PEU) showed a weak, non-significant relationship with PU (β = 0.18, p = 0.063). Facilitating Conditions significantly influenced PEU (β = 0.42, p < 0.001), as did Computer Self-Efficacy (β = 0.38, p < 0.001), with junior staff demonstrating better adaptability to new systems compared to their senior counterparts. Attitudes were moderately correlated with Behavioural Intention to Use (BI) FIS (β = 0.55, p < 0.001), while security concerns significantly affected attitudes (β = 0.46, p < 0.001), leading many employees to express scepticism about integrating advanced systems into their existing workflows. The researchers recommended that financial institutions enhance technological support through comprehensive training documentation.