ROLE OF FINTECH ACCOUNTING AUTOMATION IN MINIMIZING MANUAL ERRORS AND SUPPORTING DIGITAL MARKETING DECISION-MAKING IN FINANCIAL OPERATIONS

Abstract

Manual processing, spreadsheet workarounds, and nonstandard exception handling continue to generate posting mistakes, rework cycles, and reconciliation breaks in modern finance operations, even when organizations deploy cloud and enterprise platforms. This study therefore examined whether FinTech enabled accounting automation reduces manual errors and which technical and human factors most strongly explain that reduction. Using a quantitative, cross sectional, case-based design, data were collected via a structured survey from 210 finance and accounting professionals working in cloud integrated enterprise environments across functions such as accounts payable and receivable, general ledger, reporting, and control review, representing multiple enterprise implementation cases. The model treated Manual Error Reduction (MER) as the dependent variable and specified Automation Intensity (AI), Validation Strength (VS), Automated Control Monitoring (ACM), and Human System Fit and Compliance (HSF) as key predictors, with experience and role level included as controls. The analysis plan included descriptive statistics to profile construct levels, reliability assessment using Cronbach alpha, Pearson correlation tests to examine bivariate relationships, and multiple regression with multicollinearity diagnostics (VIF) to estimate unique predictor effects. Descriptive results indicated above midpoint adoption and capability levels (AI M=3.94, SD=0.61; VS M=3.88, SD=0.66; ACM M=3.71, SD=0.70; HSF M=3.96, SD=0.63) alongside high perceived manual error reduction (MER M=3.82, SD=0.64), with strong internal consistency (alpha 0.82 to 0.90). Correlations supported meaningful positive associations with MER (AI r=0.62, VS r=0.55, ACM r=0.49, HSF r=0.66; all p<.001). The regression model explained 54.0 percent of the variance in MER (R=0.735, R2=0.540, Adjusted R2=0.526; F(6,203)=39.72, p<.001) and showed that HSF was the strongest predictor (beta=0.35, p<.001), followed by AI (beta=0.29, p<.001), VS (beta=0.18, p=.002), and ACM (beta=0.12, p=.024); experience was also significant (beta=0.10, p=.031) while role level was marginal (p=.054), and VIF values (1.34 to 2.11) indicated acceptable multicollinearity. Overall, the findings imply that error reduction is a socio technical outcome: organizations should expand automation coverage and embedded validations, strengthen continuous control monitoring, and prioritize training and governance that improve compliant exception handling and reduce manual overrides, using operational KPIs such as correction rates, exception aging, and straight through processing to sustain benefits.