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<p>The evaluation of bank personnel performance and the allocation of rewards play a multifaceted role within financial institutions. It fosters a culture of excellence and continuous improvement, vital attributes in an industry were customer trust and satisfaction reign supreme. This study ranks and weights 360-degree performance evaluation criteria using the hesitant De Luca-Termini entropy method to objectively assess their uncertainty and importance. Reward allocation strategies are then ranked using Hesitant Fuzzy VIKOR across key criteria—individual performance, group dynamics, technical proficiency, and creativity. After establishing the importance of each strategy, game theory is applied to determine the optimal reward allocation model. Finally, the BBO- ANN algorithm is used to predict personnel performance evaluation and refine the reward strategy, offering insights into the potential impacts on overall performance. The result show <i>Reward based on Customer Satisfaction</i> (R3) with a significance rate of 0.554099 and <i>Reward based on Team Collaboration</i> (R10) with a significance rate of 0.367012 as the most effective strategies. Additionally, the highest importance rates and weight are given both to <i>Having a Critical Spirit</i> C1(w = 0.198697, SR = 0.121413) and <i>Flexibility and Acceptance of New Conditions and Resistance to Change</i> C2 (w = 0.182290, SR = 0.559499). Also the BBO-ANN model shows strong predictive accuracy for personnel performance and reward allocations, with low MAE (0.226, 0.563) and MSE (0.026, 0.531) values, demonstrating its effectiveness in applying the optimal strategy.</p>

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Evaluation of bank personnel performance and the allocation of rewards using artificial intelligence and MCDM and game theory

  • 期刊论文

  • 发表日期：

    2025年3月11日

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  • Mohammad Asl Zare,
  • Seyed Behnam Razavian

摘要

The evaluation of bank personnel performance and the allocation of rewards play a multifaceted role within financial institutions. It fosters a culture of excellence and continuous improvement, vital attributes in an industry were customer trust and satisfaction reign supreme. This study ranks and weights 360-degree performance evaluation criteria using the hesitant De Luca-Termini entropy method to objectively assess their uncertainty and importance. Reward allocation strategies are then ranked using Hesitant Fuzzy VIKOR across key criteria—individual performance, group dynamics, technical proficiency, and creativity. After establishing the importance of each strategy, game theory is applied to determine the optimal reward allocation model. Finally, the BBO- ANN algorithm is used to predict personnel performance evaluation and refine the reward strategy, offering insights into the potential impacts on overall performance. The result show Reward based on Customer Satisfaction (R3) with a significance rate of 0.554099 and Reward based on Team Collaboration (R10) with a significance rate of 0.367012 as the most effective strategies. Additionally, the highest importance rates and weight are given both to Having a Critical Spirit C1(w = 0.198697, SR = 0.121413) and Flexibility and Acceptance of New Conditions and Resistance to Change C2 (w = 0.182290, SR = 0.559499). Also the BBO-ANN model shows strong predictive accuracy for personnel performance and reward allocations, with low MAE (0.226, 0.563) and MSE (0.026, 0.531) values, demonstrating its effectiveness in applying the optimal strategy.