<![CDATA[The default case of PT Asuransi Jiwasraya reflects the weakness of investment supervision systems and risk management, particularly in detecting manipulative stocks. This study aims to implement algorithmic vigilance through machine learning to conduct early screening of investment portfolios. The method employed is a quantitative experimental approach with a case study of Jiwasraya using stock data from the Indonesia Stock Exchange for the period 2014–2019. The S.I.G.A.P (Smart Investment Governance & Analysis Protocol) model was developed based on Random Forest, integrating fundamental analysis, market anomalies, and corporate governance. Simulation results indicate that the model is capable of identifying high-risk stocks, such as MYRX and TRAM, prior to the occurrence of default. This research proves that machine learning is effective as a support tool for investment decision-making and for strengthening institutional risk management.]]>
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