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Risk Management Financial Distress Prediction and Earnings Management in Indonesian Banks

APTISI Transactions on Management (ATM) · Vol. 10 No. 3 (2026) · pp. 318–331

DOI
10.33050/atm.v10i3.2655
ISSN
2622-6804, 2622-6812
Published
2026-08-24
Publisher
Pandawan
Language
en
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The risks faced by financial institutions, particularly banks, can influence financial performance, institutional stability, and managerial reporting behavior. As financial intermediaries and key institutions supporting economic growth, banks require effective risk management to maintain financial resilience. This study aims to examine the relationship between bank risk management, financial distress prediction, and earnings management practices in commercial banks listed on the Indonesia Stock Exchange. The study covers the 2019–2022 period, including pre-pandemic conditions, the COVID-19 disruption, and the early recovery phase. Using a quantitative approach with panel data regression analysis supported by EViews software, this study analyzes 27 commercial banks selected through purposive sampling based on complete annual report data. Bank risk management is represented by credit risk, market risk, liquidity risk, and operational risk, while financial distress and earnings management are measured using established financial models. The results show that credit risk does not significantly affect financial distress or earnings management. Market risk significantly affects earnings management but does not influence financial distress. Liquidity risk and operational risk significantly affect both financial distress and earnings management, while financial distress significantly influences earnings management. These findings highlight that liquidity and operational efficiency are important indicators for banking risk control, early warning systems, and transparent financial governance in Indonesian listed banks.

Keywords

Collaboration network of the authors

40 authors · 97 co-authorship links · showing the 40 most connected

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  • Indonesia University of Education
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Siti FatimahAffiliation unknown2
Didi SuryadiAffiliation unknown2
Halauddin HalauddinAffiliation unknown2
Greian April PangilinanAffiliation unknown2
Muhammad Ghifari IlhamAffiliation unknown2
Laksmi AnindyatiAffiliation unknown1
Radix RascaliaAffiliation unknown1
Abdul Rohman ZakyAffiliation unknown1
Muhammad Ayub ArwinAffiliation unknown1
Ratna Utami WijayantiAffiliation unknown1
Woro Nurul SeftianingtyasAffiliation unknown1
Yasinta Dewi KristiantiAffiliation unknown1
Aulia Hervi AnggrainiAffiliation unknown1
Dwi WarliAffiliation unknown1
Agung WicaksonoAffiliation unknown1
Indah SuciatiAffiliation unknown1
Arsyil WaritsmanAffiliation unknown1
Prabawati NurhabibahAffiliation unknown1
Lina NurjanahAffiliation unknown1
Roby Syaiful UbedAffiliation unknown1
Untung RahardjaAffiliation unknown1
Mohamad RakhmansyahAffiliation unknown1
Imam Ryan MaulanaAffiliation unknown1
Rizky SebastianAffiliation unknown1
Nur AzizahAffiliation unknown1
Marviola HardiniAffiliation unknown1
Alfri AdiwijayaAffiliation unknown1
Yusuf TojiriAffiliation unknown1
Nasrul HidayatAffiliation unknown1
Cristiano Gatot WagnerAffiliation unknown1
Wahyu cahyadi setiawan wahyuAffiliation unknown1
Surya KurniawanAffiliation unknown1
Tatang HermanIndonesia University of Education1
Eritha Dewi FebriantyAffiliation unknown1
Syifa MardliyahAffiliation unknown1
Ikbal PaujiAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://ijc.ilearning.co/ATM/oai, first harvested 6 Nov 2026, last seen 6 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Risk Management Financial Distress Prediction and Earnings Management in Indonesian Banks</dc:title>
	<dc:creator xml:lang="en">Suhendra, Suhendra</dc:creator>
	<dc:creator xml:lang="en">Limajatini, Limajatini</dc:creator>
	<dc:creator xml:lang="en">Rodriguez, Marta</dc:creator>
	<dc:creator xml:lang="en">Komara, Maulana Arif</dc:creator>
	<dc:subject xml:lang="en">Risk Management</dc:subject>
	<dc:subject xml:lang="en">Financial Distress</dc:subject>
	<dc:subject xml:lang="en">Earnings Management</dc:subject>
	<dc:subject xml:lang="en">Operational Risk</dc:subject>
	<dc:subject xml:lang="en">Commercial Banks</dc:subject>
	<dc:description xml:lang="en">The risks faced by financial institutions, particularly banks, can influence financial performance, institutional stability, and managerial reporting behavior. As financial intermediaries and key institutions supporting economic growth, banks require effective risk management to maintain financial resilience. This study aims to examine the relationship between bank risk management, financial distress prediction, and earnings management practices in commercial banks listed on the Indonesia Stock Exchange. The study covers the 2019–2022 period, including pre-pandemic conditions, the COVID-19 disruption, and the early recovery phase. Using a quantitative approach with panel data regression analysis supported by EViews software, this study analyzes 27 commercial banks selected through purposive sampling based on complete annual report data. Bank risk management is represented by credit risk, market risk, liquidity risk, and operational risk, while financial distress and earnings management are measured using established financial models. The results show that credit risk does not significantly affect financial distress or earnings management. Market risk significantly affects earnings management but does not influence financial distress. Liquidity risk and operational risk significantly affect both financial distress and earnings management, while financial distress significantly influences earnings management. These findings highlight that liquidity and operational efficiency are important indicators for banking risk control, early warning systems, and transparent financial governance in Indonesian listed banks.</dc:description>
	<dc:publisher xml:lang="en">Pandawan</dc:publisher>
	<dc:date>2026-08-24</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijc.ilearning.co/ATM/article/view/2655</dc:identifier>
	<dc:identifier>10.33050/atm.v10i3.2655</dc:identifier>
	<dc:source xml:lang="en">APTISI Transactions on Management (ATM); Vol. 10 No. 3 (2026): September; 318-331</dc:source>
	<dc:source xml:lang="id">APTISI Transactions on Management (ATM); Vol 10 No 3 (2026): September; 318-331</dc:source>
	<dc:source>2622-6804</dc:source>
	<dc:source>2622-6812</dc:source>
	<dc:source>10.33050/3j2htb49</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://ijc.ilearning.co/ATM/article/view/2655/972</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Suhendra Suhendra, Limajatini Limajatini, Marta Rodriguez, Maulana Arif Komara</dc:rights>
	<dc:rights xml:lang="en">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>