SINOMICSJOURNALSINOMICSJOURNAL

International Journal of Social Science, Education, Communication and EconomicsInternational Journal of Social Science, Education, Communication and Economics

Financial crisis is a serious threat to economic stability, so an effective early warning system is needed to detect and anticipate potential risks early on. The implementation of artificial intelligence (AI) in financial crisis early warning systems offers significant advantages through big data analysis capabilities, non-linear pattern detection, and more accurate and faster risk prediction than conventional methods. Studies have shown that machine learning and deep learning algorithms can improve crisis prediction accuracy, expand the scope of risk monitoring, and support more responsive decision-making by regulators and financial industry players. However, challenges such as data quality, security, model transparency, and human resource readiness still need to be addressed for optimal AI implementation. This study concludes that with good governance, investment in infrastructure and human resources, and adaptive regulations, AI can be a strategic tool in strengthening early warning systems and maintaining financial sector resilience in the digital era.

The implementation of artificial intelligence (AI) in financial crisis early warning systems is proven to improve the effectiveness of risk detection and mitigation in the era of digital disruption.AI can analyse financial data quickly and accurately, detect potentially risky patterns, and provide predictive solutions that support more responsive decision-making by companies and regulators.With the ability to process big data, AI also enables the identification of trends and anomalies that are not easily detected by conventional methods, so that financial risks can be better managed and potential losses can be minimised.

Penelitian lebih lanjut perlu dilakukan untuk mengeksplorasi bagaimana AI dapat diintegrasikan dengan sumber data alternatif, seperti analisis sentimen media sosial dan data transaksi real-time, untuk meningkatkan akurasi dan kecepatan deteksi dini krisis keuangan. Selain itu, studi komparatif diperlukan untuk mengevaluasi efektivitas berbagai algoritma machine learning dan deep learning dalam memprediksi berbagai jenis krisis keuangan, dengan mempertimbangkan karakteristik unik dari masing-masing krisis. Terakhir, penelitian perlu difokuskan pada pengembangan kerangka kerja regulasi yang adaptif dan komprehensif untuk memastikan penerapan AI dalam sistem peringatan dini keuangan dilakukan secara bertanggung jawab, transparan, dan akuntabel, serta melindungi kepentingan konsumen dan menjaga stabilitas sistem keuangan secara keseluruhan. Penelitian-penelitian ini diharapkan dapat memberikan kontribusi signifikan dalam memperkuat ketahanan sektor keuangan Indonesia di era digital yang semakin kompleks dan dinamis, serta meminimalkan dampak negatif dari potensi krisis keuangan di masa depan.

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