Implementation of AI-Powered Financial Risk Analytics in Predicting Financial Distress and Reliability of Financial Reporting of Public Companies in Indonesia

Abstract

Purpose: This study aims to examine the effectiveness of AI-powered financial risk analytics in predicting financial distress and enhancing the reliability of financial reporting among publicly listed companies in Indonesia.

Method: The study employs a quantitative explanatory research design using purposive sampling of Indonesia Stock Exchange listed firms that disclose the adoption of AI-based financial analytic tools. Secondary data were collected from annual reports and financial statements. Hierarchical regression analysis was used to test the direct effects of AI-powered analytics on financial distress prediction and financial reporting reliability, as well as their complementary and reinforcing effects within financial governance.

Findings: The results show that the application of machine learning and deep learning models significantly improves the accuracy of financial distress prediction and enables earlier identification of potential corporate failure. In addition, the adoption of AI-powered analytics enhances financial reporting reliability by increasing transparency and reducing human error through automated validation and anomaly detection. The observed increase in explanatory power when AI implementation intensity is incorporated confirms its role as a reinforcing mechanism in strengthening financial governance.

Implications: The findings suggest that AI-powered financial analytics can serve as a strategic tool for enhancing corporate financial resilience and reporting credibility. For Indonesian public companies, integrating AI within risk management and reporting processes can support better governance, regulatory compliance, and stakeholder confidence.

Novelty/Value: This study contributes to the financial risk and accounting literature by providing empirical evidence on the dual role of AI-powered analytics in financial distress prediction and financial reporting reliability within an emerging market context, particularly Indonesia.