10.57647/j.fomj.2025.0603.18

Predicting Efficiency Scores for Digital Transformation Management in the Banking System under Uncertainty Using Stochastic Data Envelopment Analysis and Machine Learning

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  • Mohammad Shirkhoda1,
  • Ladan Riazi*1,
  • Faramarz Fathnejad2,
  1. Department of Information Technology Management, SR.C., Islamic Azad University, Tehran, Iran
  2. Department of Information Technology Management, El.C., Islamic Azad University, Tehran, Iran

Received: 2025-07-07

Revised: 2025-09-10

Accepted: 2025-09-28

Published in Issue 2025-09-30

Published Online: 2025-09-29

Copyright (c) 2025 Mohammad Shirkhoda, Ladan Riazi, Faramarz Fathnejad (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Shirkhoda, M., Riazi, L., & Fathnejad, F. (2025). Predicting Efficiency Scores for Digital Transformation Management in the Banking System under Uncertainty Using Stochastic Data Envelopment Analysis and Machine Learning. Fuzzy Optimization and Modeling Journal (FOMJ), 6(3). https://doi.org/10.57647/j.fomj.2025.0603.18
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Abstract

Digital transformation in the banking system is presented as a comprehensive and multi-dimensional process influenced by various factors, such as customer experience, operational efficiency, innovation adoption, and risk management. These factors are recognized as the main criteria for evaluating and managing digital transformation. The present study analyzes the effectiveness of digital transformation in banking using the network data envelopment analysis method and examines the sensitivity of banks' efficiency scores to data changes. The results show that banks with higher efficiency scores and lower sensitivity are more successful in their digital transformation efforts. Additionally, neural network machine learning techniques have been used to identify hidden patterns and predict data. The presented model has predicted the data with high accuracy and has been able to explain nine key components of digital transformation.

Keywords

  • Digital Transformation Management, Efficiency, Data Envelopment Analysis, Machine Learning
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