Tinjauan Literatur Sistematik Pendekatan Machine Learning untuk Deteksi Kecurangan Laporan Keuangan
Keywords:
Deteksi Kecurangan, Laporan Keuangan, Machine learning, Ensemble learning, Deep learningAbstract
Kecurangan laporan keuangan merupakan ancaman serius terhadap integritas dan transparansi sistem pelaporan keuangan perusahaan, khususnya bagi perusahaan publik. Untuk mengatasi tantangan ini, pendekatan berbasis machine learning telah banyak dikembangkan sebagai alat bantu dalam mengidentifikasi pola-pola kecurangan secara otomatis dan akurat. Penelitian ini menyajikan tinjauan literatur sistematis terhadap berbagai metode klasifikasi yang digunakan dalam deteksi kecurangan laporan keuangan, berdasarkan 43 publikasi akademik dari tahun 2011 hingga 2025. Metode klasifikasi dikelompokkan ke dalam empat taksonomi utama: metode statistik konvensional, algoritma supervised learning, metode ensemble learning, dan metode deep learning. Studi ini juga membahas berbagai sumber data yang digunakan, faktor-faktor yang memengaruhi hasil klasifikasi, serta kelebihan dan kekurangan masing-masing pendekatan. Hasil tinjauan menunjukkan bahwa meskipun metode statistik konvensional seperti regresi logistik masih sering digunakan karena kemudahan interpretasinya, metode berbasis machine learning seperti Random Forest, XGBoost, dan LSTM memberikan performa yang lebih unggul dalam hal akurasi. Namun demikian, tantangan seperti kebutuhan komputasi yang tinggi dan keterbatasan interpretabilitas tetap menjadi perhatian. Studi ini juga mengidentifikasi arah penelitian masa depan, termasuk penggabungan data terstruktur dan tidak terstruktur, penggunaan teknik text mining pada data naratif, serta pengembangan sistem deteksi adaptif. Dengan semakin berkembangnya teknologi data dan kecerdasan buatan, pendekatan berbasis machine learning memiliki potensi besar untuk meningkatkan efektivitas dan efisiensi dalam mendeteksi kecurangan laporan keuangan.References
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