Prediction of Audit Findings Using Deep Learning with Financial and Non-Financial Data: A Case Study in Province X - Setiawan, Fery Yohan; Yuniarno, Eko Mulyanto; Rachmadi, Reza Fuad (Knowledge Engineering and Data Science, 2025)
The implementation of the audit from the local government financial statements by The Audit Board of The Republic of Indonesia (BPK RI), especially for the Province X representative, are frequently faced by the various limitations, one of them being the required audit time. At this moment, the BPK RI representative of Province X doesn’t have the tools that are able to help the accurate of sample determination for the pick test, which resulted in this study proposing the application of multi-label classification to predict the findings of financial statement (Laporan Keuangan, LK) audits based on financial and non financial ratio. The multi-label classification approach used is a traditional approach and deep learning. The model selection was based on model performance evaluation, using metrics such as accuracy, hamming loss, average precision, average recall, and F1 Score, resulting in the best model being DNN. The DNN model achieved an accuracy of 0.7728, a Hamming loss of 0.1750, an average precision of 0.8393, an average recall of 0.9120, and an F1 Score of 0.8740. The DNN model can be used to predict audit findings in determining the audit sample, thereby minimizing the limitations, particularly time constraints, often encountered during LK audits.