Journal of Industrial and Systems Engineering

Journal of Industrial and Systems Engineering

Deep Learning–Based Multi-Objective Financial Risk Minimization in Smart Supply Chain Finance

Document Type : Research Paper

Author
Department of Computer Science, University of Tabriz, Tabriz, Iran
Abstract
This study presents a novel deep learning–based multi-objective optimization framework for minimizing financial risks in smart supply chain finance. The proposed model integrates deep neural networks for dynamic credit risk prediction with evolutionary optimization algorithms such as NSGA-II to simultaneously minimize risk exposure, reduce capital costs, and enhance liquidity stability. Using synthetic and real financial data, the framework captures complex nonlinear patterns in supply chain interactions and translates them into adaptive decision-making strategies. Comparative analysis against baseline models demonstrates superior predictive accuracy, broader Pareto front coverage, and higher robustness under market fluctuations. Sensitivity analysis further confirms the model’s resilience to changes in key financial parameters such as interest rates, credit limits, and payment delays. The results highlight the potential of combining deep learning and multi-objective optimization to enable data-driven, risk-aware, and sustainable financial decision-making in digital supply chains.
Keywords
Subjects

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Volume 17, Issue 2 - Serial Number 2
Spring 2025
Pages 276-290

  • Receive Date 01 February 2024
  • Revise Date 16 April 2024
  • Accept Date 05 May 2024