PEMANFAATAN ARTIFICIAL INTELLIGENCE DALAM MITIGASI RISIKO PEMBIAYAAN BANK SYARIAH

Abstrak

Penggunaan Artificial Intelligence (AI) dalam manajemen risiko pembiayaan mampu meningkatkan akurasi penilaian kelayakan nasabah dan mendeteksi potensi gagal bayar lebih dini. Penelitian ini mengeksplorasi efektivitas machine learning dalam credit risk management pada bank syariah. Penelitian ini menggunakan metode kualitatif deskriptif melalui pendekatan studi literatur (Library Research) dengan mwnganalisis berbagai publikasi ilmiah terkini mengenai penerapan AI dan mechine learning dalam manajemen risiko pembiayaan, baik pada perbankan syariah maupun konvensional sebagai pembanding. Hasil penelitian menunjukkan bahwa AI mampu meningkatkan efisiensi dan mengurangi risiko pembiayaan bermasalah secara signifikan melalui penerapan automated credit scoring, early warnig system berbasis mechine learning, dan model algoritma seperti random forest serta Gradient Boosting yang tebukti memiliki akurasi prediksi gagal bayar yang tinggi. Selain itu, pendekatan Explainable Artificial Intelligence (XAI) memungkinkan bank syariah untuk memenuhi prinsip transparansi dan akuntabilitas dalam pengambilan keputusan pembiayaan, sejalan dengn nilai-nilai syariah. Penelitian ini merekomendasikan agar bank syarih mengembangkan model AI yang tidak hanya akurat secara prediktif, tetapi juga interpretable dan compliant terhadap prinsip syariah guna mendukung mitigasi risiko pembiayaan yang lebih proaktif efisien dan akuntabel.

The use of Artificial Intelligence (AI) in financing risk management can improve the accuracy of customer feasibility assessment and detect potential defaults at an earlier stage. This study explores the effectiveness of machine learning in credit risk management wthin Islamic banks. A descriptive qualitative method was employed through a library research approach, analyzing recent scientific publication on the application of Al and machine learning in financing risk management. Both in Islamic and conventional banking as a comparison. The results show that Al signicantly improves efficiency and reduces non-performing financing risk throught the implementation of automated credit scoring, machine learning-based early warning systems, and algorithmic models such as Random Forest and Gradient Boosting, which have proven to have high accuracy in predicting defaults risk. Furthermore, the Explainanble Artificial Intelligence (XAI) approach enables Islamic banks to maintain the principles of transparency and accountability in financing decision-making, in line with sharia values. This study recommends that Islamic banks develop AI models that are not only predictively accurate but also interpretable and sharia-compliant, in order to support more proactive, efficient, and accountable financing risk mitigation.