AI-Driven Financial Reporting Quality and Cost Optimization in Large Enterprises
Sodiq Ogunmola1*, Taiwo Justice Olorunlana1
Abstract
The aim of the present research is to investigate Artificial Intelligent (AI)’s role in enhancing the quality and cost-effectiveness of financial reporting within large corporations. Reporting processes have become more complex and data-heavy, allowing supporting technologies to become more prevalent: these technologies include machine learning, natural language processing, predictive analysis and intelligent automation-all in the service of accuracy and operational efficiency. The study maps out specific pathways by which Artificial Intelligent (AI) has the potential to influence reporting outcomes through a systematic review of pertinent literature, regulatory documents and industry case evidence. In the results chapter, we see that AI performs repetitive financial activities without intervention. As such, it diminishes human error; it reinforces internal controls by being better at detecting anomalies, and it brings clarity and consistency into the narrative disclosures. From a cost perspective, AI automation reduces processing costs (normally between 20 and 40 percent) at the back office, thereby speeding workflow and easing manual effort. However, challenges remain-gov. data governance, model validation difficulties and regulatory uncertainties. Artificial Intelligent (AI) is very much capable of enhancing financial reporting quality, alongside cost-efficiency within large enterprises, supported by a good governance framework and a high-quality data environment.
Keywords:
Artificial Intelligence; Financial Reporting Quality; Cost Optimization; Machine Learning; Intelligent Automation; Enterprise Finance; Internal Controls; Audit Analytics; Predictive Accounting; Large Organizations.