Artificial Intelligence-Enhanced Financial Management Information System for Church-Based Institutions: A Case Study of the Diocese of San Carlos
DOI:
https://doi.org/10.5281/zenodo.19151628Keywords:
Artificial Intelligence, Financial Management Information System, Machine Learning, Church Governance, Anomaly Detection, Stewardship TheoryAbstract
Faith-based institutions manage complex financial ecosystems involving diverse revenue streams. However, many diocesan financial processes remain semi-manual, limiting transparency and delaying reporting cycles. This study designed, implemented, and evaluated an Artificial Intelligence–enhanced Financial Management Information System (AI-FMIS) tailored for the Diocese of San Carlos, Philippines. The system integrates supervised machine learning for anomaly detection and automated transaction classification, alongside time-series forecasting for revenue prediction. Using a developmental and evaluative design, the platform was deployed across selected diocesan offices and assessed by 45 finance personnel and administrators. Quantitative evaluation employed system performance analytics and ISO/IEC 25010 software quality metrics. Results showed a 37% reduction in average transaction processing time, an increase in reporting accuracy from 82.1% to 94.6%, and an anomaly detection precision of 92.3%, aligning with regulatory expectations for AI-powered monitoring systems. User acceptability yielded an overall mean rating of 4.52/5. Findings indicate that AI integration significantly strengthens financial transparency, operational efficiency, and governance in church-based institutions. This research contributes to the knowledge on intelligent financial systems within nonprofit environments and proposes a scalable AI governance framework aligned with the Philippine Data Privacy Act.
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