ANALYSIS OF THE ACCURACY LEVEL OF FINANCIAL DISTRESS PREDICTION MODELS
Penulis Utama
:
Bagas Saputra
NIM / NIP
:
S432302003
×<p>Banks play a pivotal role in the global economy by channelling funds from savers to borrowers,
thus facilitating business activities and promoting economic growth. Consequently, systemic
banking crises can severely disrupt financial markets and hinder broader economic stability.
This study aims to enhance the early detection of bank distress within the Indonesian banking
sector, covering the period from 2014 to 2023. Data were gathered from publicly listed banks
on the Indonesia Stock Exchange (IDX). Four models were employed to develop predictive
frameworks: the Modified Altman model, the Ohlson model, the Zmijewski model, and the
XGBoost model. The performance of each model was assessed using Overall Predictive
Accuracy (OPA), Type I errors (misclassifying healthy banks as distressed), and Type II errors
(failing to detect distressed banks). Among these, the XGBoost model emerged as the most
effective, achieving the highest OPA and completely eliminating Type II errors. In contrast, the
traditional accounting-based models displayed lower accuracy rates and more pronounced
Type II errors, indicating a limited capacity to capture the complexities of modern banking
data. These findings underscore the growing potential of machine learning algorithms, such as
XGBoost, to substantially improve financial distress prediction and foster a more resilient
banking system. By providing banks, regulators, and policymakers with robust early warning
signals, the model proposed in this study helps mitigate the risks associated with bank failures,
thereby enhancing financial stability and supporting sustained economic resilience in Indonesia
and beyond. Additionally, future research may refine these models by incorporating
macroeconomic indicators and recalibrating traditional approaches to reflect the evolving
nature of the banking landscape.</p>
×
Penulis Utama
:
Bagas Saputra
Penulis Tambahan
:
-
NIM / NIP
:
S432302003
Tahun
:
2025
Judul
:
ANALYSIS OF THE ACCURACY LEVEL OF FINANCIAL DISTRESS PREDICTION MODELS
Edisi
:
Imprint
:
Surakarta - Fak. Ekonomi dan Bisnis - 2025
Program Studi
:
S-2 Akuntansi
Kolasi
:
Sumber
:
Kata Kunci
:
Financial Distress, Bank, Altman, Ohlson, Zmijewski, XGBoost
