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Exchange Rate as an Interactive Predictor in Artificial Intelligence Models for Financial Distress Prediction: Evidence from Iraqi Private Banks

Amjad Hameed Shukr1, Atheer Abbas Abadi1
1Department of Financial and Banking Sciences, College of Administration and Economics, Al-Iraqia University, Iraq
DOI: https://doi.org/10.70517/revcc2624129
Published: 30/09/2026
: Amjad Hameed Shukr, Atheer Abbas Abadi. Exchange Rate as an Interactive Predictor in Artificial Intelligence Models for Financial Distress Prediction: Evidence from Iraqi Private Banks. Revista Cultura Científica, 2026 Issue 24. pg. 1573-1584.

Abstract

This study examines the role of the exchange rate as an interactive predictor in artificial intelligence (AI) models for financial distress prediction in Iraqi private banks. Earlier financial-distress studies have primarily emphasized bank-specific financial ratios or have treated macroeconomic variables as additive controls. The present study instead investigates whether exchange-rate conditions modify the relationship between bank-specific financial indicators and the probability of distress. The empirical analysis uses a panel of 170 bank-year observations for 10 Iraqi private banks listed on the Iraq Stock Exchange over the period 2008–2024. Logistic Regression is used as the conventional benchmark, while Decision Tree, Random Forest, and Artificial Neural Network models are used as machine-learning alternatives; Cox proportional hazards analysis is additionally used to examine interaction effects in a time-to-distress framework. The comparative results show that Random Forest achieved the highest reported classification performance, with an accuracy of 98.1%, sensitivity of 85.7%, specificity of 100%, F1-score of 92.3%, and Cohen’s Kappa of 0.898. Interaction analysis indicates that the exchange rate significantly moderates the association between non-performing loans and financial distress and also modifies the effect of selected profitability indicators. Decision-tree rules further reveal exchange-rate thresholds that alter distress classification conditional on profitability and credit quality. Variable-importance analyses consistently place profitability, credit quality, and the exchange rate among the most influential predictors. These findings support an interaction-based interpretation of banking risk in which macroeconomic conditions change the informational value of bank-specific indicators rather than merely adding an independent source of risk. The study contributes to the financial-distress literature by integrating model comparison, interaction analysis, and interpretable

Keywords: financial distress, artificial intelligence, random forest, exchange rate, banking risk, Iraqi banks

Abstract

This study examines the role of the exchange rate as an interactive predictor in artificial intelligence (AI) models for financial distress prediction in Iraqi private banks. Earlier financial-distress studies have primarily emphasized bank-specific financial ratios or have treated macroeconomic variables as additive controls. The present study instead investigates whether exchange-rate conditions modify the relationship between bank-specific financial indicators and the probability of distress. The empirical analysis uses a panel of 170 bank-year observations for 10 Iraqi private banks listed on the Iraq Stock Exchange over the period 2008–2024. Logistic Regression is used as the conventional benchmark, while Decision Tree, Random Forest, and Artificial Neural Network models are used as machine-learning alternatives; Cox proportional hazards analysis is additionally used to examine interaction effects in a time-to-distress framework. The comparative results show that Random Forest achieved the highest reported classification performance, with an accuracy of 98.1%, sensitivity of 85.7%, specificity of 100%, F1-score of 92.3%, and Cohen’s Kappa of 0.898. Interaction analysis indicates that the exchange rate significantly moderates the association between non-performing loans and financial distress and also modifies the effect of selected profitability indicators. Decision-tree rules further reveal exchange-rate thresholds that alter distress classification conditional on profitability and credit quality. Variable-importance analyses consistently place profitability, credit quality, and the exchange rate among the most influential predictors. These findings support an interaction-based interpretation of banking risk in which macroeconomic conditions change the informational value of bank-specific indicators rather than merely adding an independent source of risk. The study contributes to the financial-distress literature by integrating model comparison, interaction analysis, and interpretable

Keywords: financial distress, artificial intelligence, random forest, exchange rate, banking risk, Iraqi banks

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Amjad Hameed Shukr
Department of Financial and Banking Sciences, College of Administration and Economics, Al-Iraqia University, Iraq
amjad.h.shukr@aliraqia.edu.iq
Atheer Abbas Abadi
Department of Financial and Banking Sciences, College of Administration and Economics, Al-Iraqia University, Iraq

How to cite:

Amjad Hameed Shukr, Atheer Abbas Abadi. Exchange Rate as an Interactive Predictor in Artificial Intelligence Models for Financial Distress Prediction: Evidence from Iraqi Private Banks. Revista Cultura Científica, 2026 Issue 24. pg. 1573-1584.

Publication History

  • Received: 21/06/2026
  • Accepted: 09/09/2026
  • Published: 30/09/2026
Copyright © 2026, Amjad Hameed Shukr, Atheer Abbas Abadi. Published by Revista Cultura Científica. This article is published as open access under the Creative Commons Attribution 4.0 International (CC BY 4.0) license (http://creativecommons.org/licenses/by/4.0/).

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