Audit Kredit Digital Berbasis Explainable Artificial Intelligence (XAI): Tinjauan Pustaka Sistematis

Muhammad Arief Sutisna; Imam Riadi; Abdul Fadlil

doi:10.54914/jtt.v12i1.2614

Penulis

Muhammad Arief Sutisna universtas saintek muhammadiyah
Imam Riadi Universitas Ahmad Dahlan
Abdul Fadlil Universitas Ahmad Dahlan

DOI:

https://doi.org/10.54914/jtt.v12i1.2614

Kata Kunci:

Audit Kredit, Explainable Artificial Intelligence, LIME, SHAP, Systematic Literature Review

Abstrak

Transformasi digital pada sektor keuangan mendorong penerapan Artificial Intelligence (AI) dalam proses audit kredit. Meskipun AI mampu meningkatkan kecepatan dan akurasi penilaian risiko, model‑model modern seperti deep learning bersifat black‑box sehingga menimbulkan masalah transparansi dan akuntabilitas—dua hal yang sangat penting dalam audit kredit yang harus mematuhi regulasi dan membangun kepercayaan pemangku kepentingan. Penelitian ini menggunakan pendekatan Systematic Literature Review (SLR) untuk menyintesis literatur ilmiah yang membahas penerapan Explainable Artificial Intelligence (XAI) dalam audit kredit digital. Proses SLR meliputi: Perumusan query pencarian (mis. “explainable AI”, “credit audit”, “model interpretability”), Penyaringan studi berdasarkan relevansi, kualitas metodologis, dan rentang tahun publikasi, Ekstraksi data utama (metode XAI, jenis dataset, model prediksi, alat yang dipakai, serta temuan kunci), dan Analisis sintesis komparatif. Berdasarkan Systematic Literature Review ditemukan bahwa metode XAI utama yaitu SHAP dan LIME, model prediksi yang paling sering dipakai adalah Random Forest dan XGBoost dan beberapa kelemahan yang berulang ditemukan, antara lain keterbatasan representativitas dataset, risiko over‑fitting, serta trade‑off antara tingkat akurasi dan tingkat interpretabilitas. Dengan demikian, integrasi XAI yang tepat diharapkan dapat meningkatkan transparansi, keadilan, dan kepercayaan dalam audit kredit digital berbasis AI, sekaligus membuka jalan bagi praktik audit yang lebih etis dan selaras dengan tuntutan regulasi.

Unduhan

Data unduhan belum tersedia.

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