An Advanced Regulatory Technology Framework for Improving Financial Transparency and Fraud Reporting Accuracy

Abstract

Financial transparency and fraud detection have become critical concerns for regulators, financial institutions, and stakeholders in the modern digital economy. The rise of complex financial instruments, cross-border transactions, and sophisticated fraud schemes necessitates the development of advanced regulatory technology (RegTech) frameworks capable of ensuring accurate reporting, real-time monitoring, and compliance enforcement. This paper presents a conceptual and systematic approach for designing a RegTech framework aimed at improving financial transparency and enhancing fraud reporting accuracy. It synthesises developments in artificial intelligence, blockchain, machine learning, data analytics, and digital reporting, highlighting their roles in strengthening regulatory oversight. The framework emphasises the integration of risk-based analytics, automated compliance monitoring, and continuous feedback mechanisms to improve decision-making and reduce operational inefficiencies. A critical literature review is conducted to explore recent advances in fraud detection, transaction monitoring, audit automation, and regulatory compliance. The study identifies current gaps in regulatory approaches and proposes an integrated framework that combines technological, procedural, and regulatory perspectives. This paper contributes to the growing body of knowledge on RegTech and provides practical guidance for regulators, financial institutions, and technology developers seeking to implement robust, accurate, and transparent financial monitoring systems.