Leveraging artificial neural networks and analytical hierarchy process for business strategy evaluation in banking

Аннотация

This study explores the integration of Artificial Neural Network (ANN) and Analytical Hierarchy Process COPYRIGHT © 2025 Original content from this work may be used under the terms (AHP) as a tool for estimating and evaluating the of the creative commons attributes 4.0 License. business strategy of banks. The increasing complexity and dynamism of the banking industry necessitate the use of advanced decision-making techniques that can process large amounts of data and provide insightful recommendations for strategic decisions. In this research, AHP is employed to prioritize various factors influencing business strategy, while ANN is used to model the relationship between these factors and the bank's performance. By integrating these two techniques, this paper aims to provide a robust model that helps in estimating the effectiveness of different business strategies in the banking sector. The results demonstrate that the ANN-AHP integration can offer valuable insights for strategy formulation, improve decision-making accuracy, and enhance business performance in a competitive banking environment. Keywords: Artificial Neural Networks, Analytical Hierarchy Process, Business Strategy, Bank Strategy Evaluation, Decision-Making, Hybrid Model, Strategic Planning, Banking Industry, Machine Learning, Multi- Criteria Decision-Making. Introduction: In the modern banking industry, strategic decision-making is a critical process for achieving competitive advantage and ensuring long-term sustainability. Traditional methods of estimating business strategies may not fully account for the complex, dynamic, and multi-criteria nature of modern banking environments. As such, advanced decision European International Journal of Multidisciplinary Research 01 https://eipublication.com/index.php/eijmrms and Management Studies European International Journal of Multidisciplinary Research and Management Studies support tools that can integrate diverse information substantial value. and evaluate strategies in a structured manner are Artificial Neural Networks (ANNs) are a type of machine highly sought after. learning model that excels in recognizing complex Artificial Neural Networks (ANNs) and the Analytical patterns and relationships within large datasets. These Hierarchy Process (AHP) are two powerful techniques networks are designed to simulate the way the human that can assist in making more informed decisions. brain processes information and can learn from data to ANN, a form of machine learning, can identify complex make predictions or classifications. In the context of patterns and relationships in large datasets, while AHP banking, ANN has been applied to various tasks, such as provides a structured framework for decision-making predicting loan defaults, analyzing customer behavior, by organizing criteria in a hierarchical structure and and identifying trends in market performance. One of assigning relative importance to each criterion. the strengths of ANN is its ability to handle non-linear relationships between inputs, making it well-suited for This study seeks to combine these two methods to dynamic, real-world problems where interactions estimate and evaluate the business strategy of a bank. between variables are intricate and complex. By integrating AHP with ANN, this research aims to develop a model that can assess the effectiveness of However, while ANNs are powerful at identifying different strategies based on various criteria, such as patterns and predicting outcomes, they may not always market positioning, profitability, customer provide a clear framework for structuring and satisfaction, and risk management. This integrated prioritizing decision criteria, particularly in situations model is expected to enhance the decision-making where subjective judgments and qualitative factors are process, providing bank managers with a more robust important. This is where the Analytical Hierarchy tool for strategic planning. Process (AHP) comes into play. AHP, developed by Thomas Saaty in the 1980s, is a multi-criteria decision- In the contemporary banking industry, where making method that enables decision-makers to competition is fierce and market conditions are structure complex problems by breaking them down constantly changing, strategic decision-making plays a into smaller, more manageable components. In AHP, critical role in determining a bank’s success or failure. decision-makers perform pairwise comparisons of The banking environment is characterized by complex criteria and sub-criteria to determine their relative factors, including regulatory changes, technological importance. The method then aggregates these advancements, consumer preferences, and economic judgments into a set of priorities, which can guide fluctuations, making it increasingly difficult for banks decisions on selecting the best course of action. to formulate effective business strategies. Strategic decisions made by banks must, therefore, take into Whi