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A merchant analytics framework for revenue forecasting and financial stress detection using transaction data

IAES International Journal of Artificial Intelligence · Vol. 14 No. 6 (2025) · pp. 4848–4864

DOI
10.11591/ijai.v14.i6.pp4848-4864
ISSN
2252-8938, 2089-4872
Published
2025-12-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

By processing payments and providing specialized financial services, acquiring banks are essential for merchants’ operations. To forecast 30-day revenue trajectories, identify seasonal demand patterns, and identify early indicators of financial stress, this paper presents a scalable merchant analytics framework that benefits from transactional data. The framework captures multi-level seasonalities using Prophet time series model, allowing dynamic product offerings like revenue-based loans. Proactive risk management is supported offerings like revenue-based loans. Proactive risk management is supported. by a new stress-flagging mechanism that identifies merchants at risk based on deviations in revenue trends. The framework achieved a median 30-day mean absolute percentage error (MAPE) of 56.51% after the validation on a dataset with 130,350 transactions from 460 merchants in a volatile economic environment. The model demonstrated significant practical utility in identifying financial distress and segmenting merchant behavior, despite its moderate predictive precision, which is common challenge in high-variance merchant datasets. Model outputs are converted into decision-support visualizations along with an interactive dashboard.

Keywords

Collaboration network of the authors

32 authors · 87 co-authorship links · 50 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Vignan's Foundation for Science, Technology & Research
  • Lebanese American University
  • Soonchunhyang University
  • Al-Mustaqbal University
  • Hindustan Institute of Technology and Science
  • Higher Colleges of Technology
  • Beirut Arab University
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Maytham N. MeqdadAl-Mustaqbal University6
Yunyoung NamSoonchunhyang University5
Lakshmana Kumar RamasamyHindustan Institute of Technology and Science4
Firoz KhanHigher Colleges of Technology4
R. Lakshmana KumarHindustan Institute of Technology and Science2
Saif O. HusainIraqi University2
Alyaa Mohammed JawadAl-Mustaqbal University2
A. SaravananAffiliation unknown1
S. Sathya BamaAffiliation unknown1
Mohammed Zuhair Al-TaieUniversity of Technology Malaysia1
Joel Pinho LucasAffiliation unknown1
Jinesh AhamedHigher Colleges of Technology1
P. ChandanaVignan's Foundation for Science, Technology & Research1
G S Pradeep GhantasalaAlliance University1
J. R. V. JenyVignan's Foundation for Science, Technology & Research1
Kaushik SekaranVignan's Foundation for Science, Technology & Research1
N. DeepikaVignan's Foundation for Science, Technology & Research1
Venkatesan RajinikanthSt. Joseph’s College of Engineering1
Jamin KooSoonchunhyang University1
Byeong-Gwon KangSoonchunhyang University1
Ahlam R. KhekanUniversity of Information Technology and Communications1
Ganesh ChandrasekaranSri Eshwar College of Engineering1
M. Kalpana ChowdaryAffiliation unknown1
Jyothi Chinna BabuAffiliation unknown1
Ajmeera KiranAffiliation unknown1
Kotthuru Anil KumarAffiliation unknown1
Fardin Abdali-MohammadiRazi University1
Abdullah Hasan HusseinAffiliation 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) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">A merchant analytics framework for revenue forecasting and financial stress detection using transaction data</dc:title>
	<dc:creator>Harb, Yara</dc:creator>
	<dc:creator>Baaklini, Wissam</dc:creator>
	<dc:creator>Abbas, Nadine</dc:creator>
	<dc:creator>Kadry, Seifedine</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Financial stress detection;  Merchant analytics;  Prophet model;  Time series forecasting; Transaction data</dc:subject>
	<dc:description xml:lang="en-US">By processing payments and providing specialized financial services, acquiring banks are essential for merchants’ operations. To forecast 30-day revenue trajectories, identify seasonal demand patterns, and identify early indicators of financial stress, this paper presents a scalable merchant analytics framework that benefits from transactional data. The framework captures multi-level seasonalities using Prophet time series model, allowing dynamic product offerings like revenue-based loans. Proactive risk management is supported offerings like revenue-based loans. Proactive risk management is supported. by a new stress-flagging mechanism that identifies merchants at risk based on deviations in revenue trends. The framework achieved a median 30-day mean absolute percentage error (MAPE) of 56.51% after the validation on a dataset with 130,350 transactions from 460 merchants in a volatile economic environment. The model demonstrated significant practical utility in identifying financial distress and segmenting merchant behavior, despite its moderate predictive precision, which is common challenge in high-variance merchant datasets. Model outputs are converted into decision-support visualizations along with an interactive dashboard.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2025-12-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/29312</dc:identifier>
	<dc:identifier>10.11591/ijai.v14.i6.pp4848-4864</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 14, No 6: December 2025; 4848-4864</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v14.i6</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/29312/14861</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Yara Harb, Wissam Baaklini, Nadine Abbas, Seifedine Kadry</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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