International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 13, Issue 4 (April 2026), Pages: 174-182

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 Original Research Paper

Uncovering hidden patterns: Predicting money laundering risk using financial data

 Author(s): 

Altan-Erdene Batbayar *, Badrakh Battsengel

 Affiliation(s):

Business School, National University of Mongolia, Ulaanbaatar, Mongolia

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 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0000-0002-3255-4580

 Digital Object Identifier (DOI)

 
https://doi.org/10.21833/ijaas.2026.04.017

 Abstract

This study examines whether firm-level financial statement data can be used to identify companies involved in money laundering. Using Mongolia as a case study of a resource-rich emerging economy with institutional constraints, the research is based on a balanced panel dataset of 118 companies over the period 2013–2022. A probit regression model is applied to evaluate the predictive ability of key financial indicators, including firm size, leverage, inventory, profitability, and social insurance contributions, in detecting confirmed cases of money laundering. The results show that firms without inventory or social insurance payments, with smaller asset size, higher leverage, and consecutive net losses, are significantly more likely to be associated with illicit financial activities. These findings remain consistent across alternative model specifications and demonstrate the usefulness of accounting-based indicators as early warning tools for anti-money-laundering enforcement. This study provides one of the first empirical applications of financial statement analysis to predict money laundering risk in a developing market context. The results also offer practical implications for regulators by suggesting that automated red-flag systems based on publicly available financial reports can improve monitoring in settings with limited manual oversight. Overall, the study contributes to the literature on anti-money laundering and supports the development of data-driven compliance approaches.

 © 2026 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

Money laundering detection, Financial statement analysis, Probit regression model, Emerging markets, Illicit financial behavior

 Article history

Received 26 October 2025, Received in revised form 13 April 2026, Accepted 20 April 2026

 Acknowledgment

No Acknowledgment. 

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

Batbayar AE and Battsengel B (2026). Uncovering hidden patterns: Predicting money laundering risk using financial data. International Journal of Advanced and Applied Sciences, 13(4): 174-182

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