<![CDATA[The background of this research is the challenge in supervising goods and services procurement in the public sector, which is still dominated by traditional, reactive auditing methods conducted after transactions are finalized. The primary issue is the high volume of transactions and data complexity, which hinders early fraud detection. This research aims to design and develop an early warning system using Continuous Auditing and Continuous Monitoring (CACM) methods to enhance the effectiveness of fraud detection. The research method involves system development based on data integration from the Electronic Procurement System (SPSE) and other supporting monitoring systems. By utilizing data analytics, the system is designed to automatically identify risk indicators based on tender winner patterns and bidding behavior. The results indicate that CACM implementation enables real-time anomaly identification, providing early warning signals for auditors to take preventive measures before broader irregularities occur. In conclusion, the application of the CACM system transforms the internal oversight paradigm into a more proactive approach, strengthening fraud detection capabilities while improving accountability and transparency in government procurement processes]]>
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