AI redefines financial transparency and risk control in corporate sectors

AI adoption significantly influences how corporate boards operate, according to the study’s regression analysis. With an R² of 0.582, the study confirms that AI-driven tools such as predictive analytics, anomaly detection, and real-time data dashboards enhance the speed, accuracy, and transparency of board-level decisions. These tools mitigate information asymmetry between executives and shareholders - an enduring issue in Agency Theory - and foster more aligned, data-driven strategic choices.

Artificial intelligence is reshaping corporate accountability in developing economies, with new empirical evidence confirming its critical role in transforming financial transparency and governance systems. A new peer-reviewed study titled “AI-Driven Financial Transparency and Corporate Governance: Enhancing Accounting Practices with Evidence from Jordan,” published in Sustainability, explores how AI technologies enhance risk management, decision-making, regulatory compliance, and stakeholder engagement in Jordan's business ecosystem.

The study, based on a large-scale survey of 564 professionals from diverse sectors, offers statistical evidence that AI improves board-level decision-making, enhances risk management strategies, elevates financial transparency, and boosts stakeholder engagement and executive compensation effectiveness. These findings signal an urgent transformation underway in how financial oversight and corporate control mechanisms operate in emerging markets.

To what extent does AI improve governance structures and board-level decision-making?

AI adoption significantly influences how corporate boards operate, according to the study’s regression analysis. With an R² of 0.582, the study confirms that AI-driven tools such as predictive analytics, anomaly detection, and real-time data dashboards enhance the speed, accuracy, and transparency of board-level decisions. These tools mitigate information asymmetry between executives and shareholders - an enduring issue in Agency Theory - and foster more aligned, data-driven strategic choices. The findings support the theoretical argument that AI not only augments decision-making but actively reduces governance inefficiencies within the boardroom.

Multiple regression and SmartPLS analysis also highlight the strength of AI’s contribution across composite governance indicators. For instance, the path coefficient linking AI impact to executive compensation effectiveness was 0.915 - evidence that boardrooms are increasingly relying on automated performance metrics and transparency-enhancing AI to guide executive oversight and incentive structures.

How does AI influence transparency, risk management, and internal controls?

Financial transparency and internal controls are key governance pillars, and the study confirms that AI strengthens both. AI systems automate time-sensitive accounting tasks, facilitate rule-based anomaly detection, and generate real-time financial insights. These tools ensure accurate reporting and flag potential fraud, aligning closely with risk mitigation strategies. The study's regression analysis showed a strong correlation between AI use and risk management (R² = 0.502), with tools such as machine learning models being used to detect irregularities and prevent fraud.

Interestingly, while transparency was statistically significant in simple regression (R² = 0.562), it showed reduced influence in the multiple regression model, indicating that transparency might be a mediating factor rather than a direct driver when other governance variables like executive oversight and stakeholder engagement are accounted for. The authors suggest that transparency’s influence might be absorbed or moderated by more dominant variables within AI-governed ecosystems.

The Al-Wasleh case study included in the research further exemplifies AI’s real-world application. By deploying AI-powered credit scoring and ERP-integrated accounting systems, Al-Wasleh enhanced transparency and compliance while overcoming technical and cultural challenges, including data inconsistencies and staff resistance. This real-life example validates the broader quantitative findings and supports the theoretical link between AI and accountability structures.

What regulatory and ethical implications arise from AI integration in governance?

Despite promising results, the study outlines persistent risks associated with AI integration. Bias in algorithms, lack of data privacy safeguards, and insufficient explainability mechanisms are top concerns, particularly in emerging markets like Jordan, where regulatory infrastructure remains underdeveloped. The study emphasizes that AI systems can inherit and reinforce existing inequities unless trained on diverse, unbiased datasets and governed by transparent policies.

Data privacy risks are exacerbated by limited enforcement of standards akin to the EU’s General Data Protection Regulation (GDPR). AI decisions are often black-boxed, complicating accountability and auditability. The study calls for explainable AI (XAI) protocols, algorithmic audits, and corporate AI ethics committees to bridge the transparency gap and support sustainable, ethical AI adoption in governance systems.

From an institutional theory perspective, the findings underscore how global regulatory pressures and legitimacy demands are pushing firms in Jordan to adopt AI. This adoption is not solely about technological advancement but also reflects a response to evolving compliance landscapes and investor expectations for ethical governance.