Automated Financial Reconciliation Systems for Enhancing Efficiency and Transparency in Enterprise Accounting Workflows

Abstract

Financial reconciliation in large enterprises is often constrained by manual matching, fragmented cloud and ERP integrations, and inconsistent evidence retention, which prolong the close and elevate audit risk. This study examined whether Automated Financial Reconciliation Systems (AFRS) capabilities improve workflow efficiency and workflow transparency in cloud enabled enterprise accounting cases. Using a quantitative, cross sectional, case study-based design, survey data were collected from N = 186 finance and accounting users involved in reconciliation preparation, review, close support, and control oversight. Independent variables were Automation Capability, Integration Quality, Data Quality Readiness, Audit Trail and Control Integration, and User Competency and Training; dependent variables were Workflow Efficiency and Workflow Transparency. The analysis plan applied descriptive statistics, Cronbach’s alpha, Pearson correlations, and multiple regression. Audit Trail and Control Integration (M = 4.02) and transparency (M = 4.01) were rated highest, and efficiency was also favorable (M = 3.87). All constructs were reliable (α = 0.81 to 0.91). Correlations were positive and significant, with transparency most strongly associated with Audit Trail and Control Integration (r = 0.71, p < .01) and efficiency most strongly associated with Automation Capability (r = 0.62, p < .01). The efficiency model explained 52% of variance (R² = 0.52) with significant effects from Automation Capability (β = 0.36, p < .001), Integration Quality (β = 0.24, p = .002), Data Quality Readiness (β = 0.19, p = .008), and User Competency and Training (β = 0.14, p = .031). The transparency model explained 63% of variance (R² = 0.63) and was driven primarily by Audit Trail and Control Integration (β = 0.46, p < .001), with additional effects from Data Quality Readiness (β = 0.23, p = .001) and Integration Quality (β = 0.17, p = .009). These findings imply that faster closes require strong automation plus integration and data readiness, while audit readiness depends most on embedded control and audit trail features, reinforced through targeted training where agreement was weakest (Top 2 Box = 52%) in practice.