The Impact of automation on bank reconciliation processes: a case study of CABS.

Abstract

This study examines the impact of automation on bank reconciliation processes and the resulting effects on cash flow management and financial health at the Central Africa Building Society (CABS) in Zimbabwe, responding to the limited empirical evidence on automated reconciliation within macro economically volatile, emerging-market contexts. Anchored on the Technology Acceptance Model, Agency Theory and the Baumol Cash Management Model, which respectively explain the adoption of reconciliation technology, the internal-control rationale for continuous monitoring, and the imperative of optimal liquidity management, the study adopted a mixed-methods case study design in which 50 finance, treasury and IT staff completed structured questionnaires (an 86% response rate) and seven of ten targeted senior personnel were interviewed; quantitative data were analysed descriptively in SPSS Version 27 with reliability confirmed by Cronbach's Alpha (overall α = 0.912), while qualitative data were analysed thematically. The findings show that automated reconciliation is approximately 70-85% faster than manual methods, achieves match rates above 99%, and gives real-time visibility of the cash position, which strengthens regulatory compliance, reduces fraud risk and supports institutional solvency; the alternative hypothesis that automation significantly improves the accuracy, efficiency, cash flow management and financial health of bank reconciliation at CABS was accepted across all analytical dimensions. The study recommends that CABS extend automation to branch level, formalise staff training and reconciliation governance policies, and invest in API-based real-time system integration, and it further recommends that future research pursue comparative, multi-institutional studies across Zimbabwean banks and a formal cost-benefit analysis of reconciliation automation to validate and extend these findings beyond a single case study.