Business Process Improvement in Finance Operations through Predictive Analytics and Decision Intelligence

Abstract

Finance operations are increasingly required to accelerate closing cycles, improve forecast accuracy, strengthen controls, and provide decision support without increasing administrative costs. Predictive analytics enables the anticipation of cash shortfalls, late payments, atypical transactions, workload peaks, and close delays. However, predictions alone do not improve processes unless they are translated into governed actions. This review analyses how predictive analytics and decision intelligence jointly enable business process improvement across procure-to-pay, order-to-cash, record-to-report, financial planning and analysis, treasury, and compliance. A structured integrative review of thirty publications from 2020 to 2025 synthesises research on business intelligence, process management, machine learning, process mining, automation, and responsible financial analytics. The analysis identifies four mechanisms of improvement: earlier exception detection, dynamic prioritisation, resource and working-capital optimisation, and closed-loop learning from decision outcomes. It further finds that value creation relies more on data quality, workflow integration, explainability, accountability, and feedback design than on model sophistication. This paper presents a Finance Decision Intelligence Improvement Framework that links event data, predictive systems, decision rules, human judgement, automated execution, and performance monitoring. The framework distinguishes prediction quality from decision quality and process value, thereby reducing the risk of technically accurate models that fail operationally. The review concludes that predictive analytics achieves sustainable finance improvement when integrated as a controlled decision service rather than as a stand-alone dashboard.