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<p>Adverse selection, driven by asymmetric information, poses significant challenges to resource allocation and decision-making across various business sectors. In banking and insurance, it occurs when one party lacks knowledge about the other’s creditworthiness or risk profile. In resource-limited industries, uncertainty in customers’ behavior further complicates decision-making. Artificial intelligence (AI) has emerged as a powerful tool for mitigating asymmetric information, thereby reducing adverse selection. AI technologies can effectively analyze large datasets, offer risk assessments, and predict customer behavior. Specifically, supervised AI learning leverages large datasets to model complex relationships, making it practical for predictive tasks. Conversely, unsupervised learning can uncover hidden patterns in real-time and learn customer behavior through interactions with the operating environment. This allows the business operator to adapt its decision-making process, providing the scalability needed for real-time applications. However, AI deployment faces several challenges, including data quality, interpretability, biases, ethical concerns, and implementation costs. This paper explores how AI enhances information symmetry to mitigate adverse selection’s harmful effects on business performance. We highlight AI’s advantages, examine key implementation challenges, and discuss potential solutions. We present two case studies to demonstrate how AI-driven solutions bridge information gaps. The first case-study examines reinforcement learning (RL) in a drone-based dispatch telecommunication system operating under uncertain customer behavior and distribution. By learning users’ behavior, the RL agent improves information symmetry and optimizes resource allocation. Simulation results reveal that the RL can significantly enhance the achieved profitability through intelligent resource allocation decisions. In the second case study, we examine the application of supervised neural networks in banking to utilize existing customer information to improve loan allocation and reduce adverse selection risks. Simulation results reveal that supervised neural networks can develop a predictive model that accurately estimates the probability of customer churn and reduces risks.</p>

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Artificial intelligence and information symmetry: a path to mitigate adverse selection

  • 期刊论文

  • 发表日期：

    2025年8月30日

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  • Haythem Bany Salameh,
  • Ghaleb Elrefae,
  • Shorouq Eletter

摘要

Adverse selection, driven by asymmetric information, poses significant challenges to resource allocation and decision-making across various business sectors. In banking and insurance, it occurs when one party lacks knowledge about the other’s creditworthiness or risk profile. In resource-limited industries, uncertainty in customers’ behavior further complicates decision-making. Artificial intelligence (AI) has emerged as a powerful tool for mitigating asymmetric information, thereby reducing adverse selection. AI technologies can effectively analyze large datasets, offer risk assessments, and predict customer behavior. Specifically, supervised AI learning leverages large datasets to model complex relationships, making it practical for predictive tasks. Conversely, unsupervised learning can uncover hidden patterns in real-time and learn customer behavior through interactions with the operating environment. This allows the business operator to adapt its decision-making process, providing the scalability needed for real-time applications. However, AI deployment faces several challenges, including data quality, interpretability, biases, ethical concerns, and implementation costs. This paper explores how AI enhances information symmetry to mitigate adverse selection’s harmful effects on business performance. We highlight AI’s advantages, examine key implementation challenges, and discuss potential solutions. We present two case studies to demonstrate how AI-driven solutions bridge information gaps. The first case-study examines reinforcement learning (RL) in a drone-based dispatch telecommunication system operating under uncertain customer behavior and distribution. By learning users’ behavior, the RL agent improves information symmetry and optimizes resource allocation. Simulation results reveal that the RL can significantly enhance the achieved profitability through intelligent resource allocation decisions. In the second case study, we examine the application of supervised neural networks in banking to utilize existing customer information to improve loan allocation and reduce adverse selection risks. Simulation results reveal that supervised neural networks can develop a predictive model that accurately estimates the probability of customer churn and reduces risks.