The Role of Behavioral Analysis in Improving ALM for Retail Banking

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@article{1703641,
      author = {Olayinka Abiola-Adams, Chima Azubuike, Aumbur Kwaghter Sule, Richard Okon},
      title = {The Role of Behavioral Analysis in Improving ALM for Retail Banking},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {1},
      pages = {758-771},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703641.pdf},
      abstract = {In retail banking, effective Asset and Liability Management (ALM) is essential for maintaining profitability, optimizing liquidity, and managing risk exposure. However, traditional ALM approaches often overlook the critical role of customer behavior, which significantly influences balance sheet dynamics. This review explores the integration of behavioral analysis into ALM strategies to enhance financial performance and risk management for retail banks. By leveraging insights from behavioral finance, banks can better predict customer actions, such as deposit withdrawals, loan prepayments, and interest rate sensitivity, thereby making more informed decisions about asset allocation, liquidity buffers, and interest rate risk management. Key areas examined include optimizing deposit management by understanding patterns of deposit stickiness and withdrawal behaviors, as well as enhancing loan portfolio performance through behavioral credit scoring and segmentation. Behavioral analysis also improves liquidity management by forecasting cash flow fluctuations based on customer spending patterns, enabling banks to proactively adjust liquidity reserves and mitigate potential shortfalls. Additionally, understanding customer responses to interest rate changes allows for more precise hedging and repricing strategies. Technological advancements, such as big data analytics, machine learning, and artificial intelligence, have made it possible to capture and analyze large volumes of behavioral data, offering deeper insights into customer preferences and actions. Case studies presented demonstrate how banks have successfully implemented behavioral analysis to optimize their ALM practices, resulting in enhanced profitability and reduced risk. The review concludes by discussing future trends in personalized banking and the integration of behavioral insights into digital platforms, which are expected to further transform ALM in retail banking. By adopting a behavior-centric approach, retail banks can achieve a more resilient and agile balance sheet, better aligned with market conditions and customer needs.},
      keywords = {Behavioral Analysis, ALM, Retail Banking, Review},
      month = {July},
  }