Research
AI-Enabled Financial Integrity Engines: Explainable Models for Transparent Risk Assessment
As financial systems become increasingly digitized, organizations face mounting risks: algorithmic opacity, regulatory non-compliance, auditability gaps, and an erosion of institutional trust. Artificial intelligence is now a mainstream instrument for risk assessment, anomaly detection, and predictive analytics-but its rapid adoption has also exposed structural weaknesses in governance designs built on black-box models and automation-driven logic.
Integrity over pure automation
This paper argues for financial integrity systems that embed explainability, regulatory compliance, and human-centered oversight, rather than technology-first deployment strategies. Drawing on institutional economics and explainable-AI theory, it treats AI not as an independent decision-maker but as an embedded governance mechanism-one whose quality is determined by explainability, compliance-by-design, and human-in-the-loop control.
What a Financial Integrity Engine does
The governance-based approach studies how AI-enabled Financial Integrity Engines change transparency, risk containment, and integrity outcomes in financial systems. The emphasis is on unified governance: data-quality controls, decision transparency, and audit checkpoints that keep automated systems accountable to the people and regulators who rely on them.
AI in finance earns trust not by deciding alone, but by making its reasoning explainable and keeping humans in control.
The full peer-reviewed paper was published in the Scientific Journal of Bielsko-Biala School of Finance and Law (Vol. 29, No. 4, 2025).
Originally published on Scientific Journal of Bielsko-Biala School of Finance and Law. Read the original article.