European International Journal of Multidisciplinary Research
and Management Studies
01
https://eipublication.com/index.php/eijmrms
TYPE
Original Research
PAGE NO.
1-7
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SUBMITED
03 February 2025
ACCEPTED
02 March 2025
PUBLISHED
01 April 2025
VOLUME
Vol.05 Issue04 2025
COPYRIGHT
© 2025 Original content from this work may be used under the terms
of the creative commons attributes 4.0 License.
Leveraging artificial neural
networks and analytical
hierarchy process for
business strategy
evaluation in banking
Professor Clara Tan
School of Data Science and Business Strategy, Hong Kong University of
Science and Technology, Hong Kong
Abstract:
This study explores the integration of Artificial
Neural Network (ANN) and Analytical Hierarchy Process
(AHP) as a tool for estimating and evaluating the
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
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support tools that can integrate diverse information
and evaluate strategies in a structured manner are
highly sought after.
Artificial Neural Networks (ANNs) and the Analytical
Hierarchy Process (AHP) are two powerful techniques
that can assist in making more informed decisions.
ANN, a form of machine learning, can identify complex
patterns and relationships in large datasets, while AHP
provides a structured framework for decision-making
by organizing criteria in a hierarchical structure and
assigning relative importance to each criterion.
This study seeks to combine these two methods to
estimate and evaluate the business strategy of a bank.
By integrating AHP with ANN, this research aims to
develop a model that can assess the effectiveness of
different strategies based on various criteria, such as
market
positioning,
profitability,
customer
satisfaction, and risk management. This integrated
model is expected to enhance the decision-making
process, providing bank managers with a more robust
tool for strategic planning.
In the contemporary banking industry, where
competition is fierce and market conditions are
constantly changing, strategic decision-making plays a
critical role in determining a bank’s success or failure.
The banking environment is characterized by complex
factors, including regulatory changes, technological
advancements, consumer preferences, and economic
fluctuations, making it increasingly difficult for banks
to formulate effective business strategies. Strategic
decisions made by banks must, therefore, take into
account a multitude of dynamic variables and their
interrelationships. This complex decision-making
environment requires advanced tools that can process
vast amounts of data and provide meaningful insights
that support better strategic decisions.
Traditional methods of strategic planning in banks
have often relied on subjective judgments, historical
trends, and simplistic decision-making models. While
these methods may have worked in the past, they do
not account for the complex, multi-dimensional nature
of today’s business challenges. For example, strategic
decisions that rely solely on financial data may
overlook critical factors such as customer satisfaction,
technological advancements, or regulatory changes,
which could significantly impact the bank’s
performance. Furthermore, decision-makers often
face the challenge of balancing multiple, sometimes
conflicting,
objectives
—
such
as
maximizing
profitability while ensuring customer satisfaction and
managing risk. This is where advanced decision-
support tools like Artificial Neural Networks (ANN) and
Analytical Hierarchy Process (AHP) can provide
substantial value.
Artificial Neural Networks (ANNs) are a type of machine
learning model that excels in recognizing complex
patterns and relationships within large datasets. These
networks are designed to simulate the way the human
brain processes information and can learn from data to
make predictions or classifications. In the context of
banking, ANN has been applied to various tasks, such as
predicting loan defaults, analyzing customer behavior,
and identifying trends in market performance. One of
the strengths of ANN is its ability to handle non-linear
relationships between inputs, making it well-suited for
dynamic, real-world problems where interactions
between variables are intricate and complex.
However, while ANNs are powerful at identifying
patterns and predicting outcomes, they may not always
provide a clear framework for structuring and
prioritizing decision criteria, particularly in situations
where subjective judgments and qualitative factors are
important. This is where the Analytical Hierarchy
Process (AHP) comes into play. AHP, developed by
Thomas Saaty in the 1980s, is a multi-criteria decision-
making method that enables decision-makers to
structure complex problems by breaking them down
into smaller, more manageable components. In AHP,
decision-makers perform pairwise comparisons of
criteria and sub-criteria to determine their relative
importance. The method then aggregates these
judgments into a set of priorities, which can guide
decisions on selecting the best course of action.
While AHP offers a structured and rational approach to
decision-making, it relies heavily on subjective input
from decision-makers, which can sometimes introduce
biases or inconsistencies. Additionally, AHP does not
always account for the intricate relationships between
the various criteria and their impact on overall
outcomes. ANN, on the other hand, is highly effective at
modeling these relationships through data-driven
insights. Therefore, combining AHP and ANN in a
complementary manner could offer a more
comprehensive decision-making framework.
This research proposes the integration of ANN and AHP
as a tool to estimate and evaluate the business strategy
of banks. By combining the structured prioritization of
decision criteria provided by AHP with the predictive
power of ANN, this study aims to create a hybrid model
that can help bank managers evaluate different
strategies more effectively. The objective is to assess
the performance of various business strategies in a
comprehensive manner, considering both quantitative
and qualitative factors.
Through the integration of these two techniques, this
paper seeks to address the limitations of both methods
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when used individually. AHP helps to provide clarity by
prioritizing the decision criteria, and ANN aids in
predicting the outcomes based on those priorities. This
integrated approach is expected to assist banks in
making more informed, data-driven decisions that
align with their strategic goals, thereby improving their
overall performance and competitiveness in the
market.
Moreover, the use of this integrated model can guide
decision-making at various levels within the bank, from
high-level strategic planning to more tactical decisions
concerning operations, marketing, customer service,
and risk management. By offering insights into the
potential outcomes of different strategies, the ANN-
AHP model allows decision-makers to select the most
optimal strategy for the bank’s unique context and
objectives.
In this study, the focus is on the banking sector due to
its complexity and the growing need for effective
strategic management. The model presented in this
paper is intended to serve as a tool for banks to
enhance their business strategy formulation and
improve their adaptability to changing market
conditions. The integration of ANN and AHP provides a
novel approach to business strategy estimation, and
the findings could have significant implications for both
academic research and practical application in the
banking industry.
Literature Review
The banking sector has been undergoing rapid
transformations due to globalization, technological
advancements, and regulatory changes. As a result,
strategic planning in banks has become increasingly
complex. Numerous studies have examined the
application of various decision-making models to aid
banks in formulating strategies. Among these models,
AHP and ANN have gained significant attention.
AHP, developed by Saaty (1980), is a popular decision-
making method that helps in structuring complex
problems by breaking them into smaller, more
manageable parts. It involves pairwise comparisons
and assigns numerical values to the importance of each
factor. Several studies have utilized AHP to evaluate
business strategies, including those for banks (Saaty,
1990). However, AHP's reliance on subjective
judgments can introduce bias into decision-making.
On the other hand, ANN is a computational model
inspired by the biological neural networks of the brain.
It excels in identifying non-linear relationships and can
handle large volumes of data (Haykin, 1998). In the
context of banking, ANN has been applied to various
areas such as credit scoring (Thomas, 2000), loan
default prediction (Wang et al., 2004), and market
trend analysis (Zhu et al., 2006). However, ANN alone
may not always provide clear guidelines for decision-
making, especially when the decision criteria are
multifaceted.
Integrating AHP and ANN can overcome the limitations
of each individual method. While AHP helps to structure
the problem and prioritize criteria, ANN can predict the
outcome based on these prioritized criteria, creating a
more comprehensive decision-support system for
banks.
METHODOLOGY
This study adopts a hybrid approach that combines AHP
and ANN to estimate the effectiveness of different
business strategies for banks. The methodology consists
of two primary stages: the AHP process for prioritizing
decision criteria and the ANN process for predicting
business strategy outcomes.
AHP Process
1.
Identification of Criteria: The first step involves
identifying the key factors that influence a bank's
business strategy. These factors may include market
share, profitability, customer satisfaction, operational
efficiency, risk management, and innovation.
2.
Pairwise Comparison: A panel of experts from
the banking industry is consulted to perform pairwise
comparisons of the identified criteria. The AHP
methodology uses a 9-point scale to rate the relative
importance of each factor in relation to others.
3.
Weight Calculation: Using the pairwise
comparison matrix, the AHP method calculates the
weight for each criterion, which reflects its relative
importance in the overall decision-making process.
ANN Process
1.
Data Collection: Data from various sources such
as financial reports, customer satisfaction surveys, and
market research are collected. The dataset includes
historical data on bank performance under different
strategic scenarios.
2.
Model Design: A feedforward ANN with
multiple layers is designed. The input layer consists of
the weighted AHP criteria, and the output layer
represents the bank's performance under different
strategies. Hidden layers are used to capture non-linear
relationships.
3.
Training and Validation: The dataset is divided
into training and testing sets. The ANN model is trained
using backpropagation to minimize the error in
predicting the bank’s performance. The model’s
accuracy is validated using the testing set.
4.
Prediction: Once the ANN model is trained, it is
used to predict the outcomes of various business
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strategies based on the AHP-derived criteria.
Integration of AHP and ANN
The results from the AHP analysis are used as input to
the ANN model. The AHP process provides a clear
prioritization of the factors affecting business strategy,
while the ANN model predicts the bank's performance
based on these factors. The integration of these two
methods enables the evaluation of different strategies
under varying conditions, allowing for more precise
and actionable strategic insights.
RESULTS
The integration of AHP and ANN successfully
generated predictions about the effectiveness of
different business strategies for the bank under study.
The AHP process identified market share, profitability,
and customer satisfaction as the top three strategic
priorities, with risk management and innovation
following closely. These priorities were then fed into
the ANN model to estimate the potential outcomes of
various strategies, such as expanding digital banking
services, improving customer service, or focusing on
cost efficiency.
The ANN model was able to predict that a strategy
focusing on customer satisfaction and digital
innovation would lead to the highest performance
outcomes, with significant improvements in both
market share and profitability. On the other hand, a
strategy focusing solely on operational efficiency
resulted in lower overall performance, particularly in
terms of customer retention and satisfaction.
DISCUSSION
The findings demonstrate that integrating AHP and
ANN offers a powerful tool for estimating the
effectiveness of different business strategies in the
banking sector. AHP allows for the systematic
prioritization of strategic factors based on expert
judgment, ensuring that the most critical variables are
considered in the decision-making process. ANN, with
its ability to model complex relationships and predict
outcomes, provides a data-driven approach to
evaluating the potential success of various strategies.
The hybrid approach helps overcome the limitations of
both methods. While AHP provides structure and
clarity, ANN captures non-linear relationships between
the factors and strategic outcomes. By combining
these methods, banks can make more informed, data-
driven strategic decisions that are aligned with their
long-term goals.
However, there are some limitations to this approach.
The success of the model depends heavily on the
quality of the data used for training the ANN and the
accuracy of the pairwise comparisons made during the
AHP process. Future research could explore the
integration of other decision-making techniques, such
as fuzzy logic or genetic algorithms, to further enhance
the predictive accuracy of the model.
The integration of Artificial Neural Networks (ANN) and
Analytical Hierarchy Process (AHP) presents a novel and
powerful approach to strategic decision-making,
particularly in complex environments such as the
banking sector. This hybrid method allows decision-
makers to assess business strategies in a more
structured and data-driven way, overcoming the
limitations of traditional decision-making models that
rely heavily on subjective judgments or simplistic
quantitative measures. In this section, we discuss the
implications of the findings from the study, highlight the
strengths and limitations of the integrated ANN-AHP
model, and explore how the model can be further
developed and applied in the banking industry.
Enhancing Strategic Decision-Making with the ANN-AHP
Model
One of the main advantages of combining AHP with ANN
is the ability to manage both qualitative and
quantitative decision criteria. AHP provides a structured
framework for decision-makers to identify, prioritize,
and weigh the criteria that influence business strategy
in a bank. For example, AHP allows experts to rank
strategic factors such as customer satisfaction, market
share, profitability, risk management, and technological
innovation in terms of their importance. However, while
AHP offers valuable insights into how different criteria
relate to each other, it does not capture the complex
relationships between these factors in a dynamic and
evolving environment.
This is where the ANN component comes in. ANN, as a
machine learning model, is capable of identifying
complex, non-linear relationships within large datasets.
By training the ANN model with historical data on a
bank’s performance, the model learns the interactions
between different factors (such as how improvements
in customer satisfaction might impact market share or
profitability). The ANN model then predicts the
potential outcomes of different strategic alternatives
based on the prioritized criteria derived from AHP. This
integration of both methods creates a more robust
decision-making framework, where strategic choices
are based not only on expert judgment (via AHP) but
also on data-driven predictions (via ANN).
The hybrid model also allows banks to simulate the
effects of different strategies before implementing
them. For example, a bank might want to assess the
potential outcomes of investing in digital banking
services versus expanding traditional brick-and-mortar
branches. The AHP component would help prioritize the
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factors, such as market trends, customer demand, and
competitive pressures, that affect these strategies. The
ANN component would then predict the likely
performance of each strategy under different
scenarios, such as varying levels of customer adoption
or changes in the competitive landscape. This
predictive capability makes the decision-making
process more informed and reduces the uncertainty
inherent in strategic planning.
Practical Applications for the Banking Sector
The banking industry operates in an environment of
constant change, with factors such as technological
advancements, regulatory changes, and shifting
customer expectations influencing business strategy.
As a result, banks need a decision-making process that
is adaptable, data-driven, and capable of integrating
multiple strategic factors. The integration of AHP and
ANN can help banks address these challenges by
offering several practical benefits:
1.
Data-Driven Insights: The ANN component
enables the use of large datasets, which are abundant
in the banking sector, such as customer feedback,
transaction data, market performance, and financial
reports. The model can be trained to recognize trends
and patterns that might not be apparent through
traditional analytical methods. By learning from past
data, the model can predict the likely outcomes of
different strategies based on these trends, allowing
decision-makers to make more accurate projections
about the future.
2.
Holistic Strategy Formulation: Banks face the
challenge of balancing multiple objectives, such as
maximizing profits, improving customer satisfaction,
and managing risks. Traditional strategic planning
methods may focus on one or two factors at the
expense of others. The ANN-AHP integration allows
banks to evaluate how different factors interact with
each other, leading to a more holistic understanding of
the potential impact of different strategies.
3.
Scenario Planning and Simulation: One of the
key advantages of using ANN is its ability to perform
scenario analysis and predict outcomes under different
conditions. This is particularly useful in the banking
industry, where external factors like interest rates,
economic conditions, and technological disruption can
change rapidly. By integrating AHP and ANN, banks can
test different scenarios (e.g., an economic downturn,
increased digital adoption, or a change in regulation)
and assess how these changes might affect their
business strategies.
4.
Strategic Prioritization: AHP helps banks
prioritize which factors are most critical in shaping
their business strategy. For example, a bank may
identify customer satisfaction as the most important
criterion for success in a competitive market. Once this
criterion is prioritized, the ANN model can predict how
changes in customer satisfaction (e.g., through
improved customer service or the introduction of new
digital services) would impact the bank’s overall
performance. This prioritization ensures that strategic
efforts are focused on areas that matter most to the
bank’s success.
5.
Risk Management: Banks face significant risks
from economic volatility, regulatory changes, and
market disruptions. The ANN-AHP model can be used to
evaluate the potential risks associated with different
strategies. For example, the model can simulate the risk
of a particular strategy under different market
conditions, helping banks anticipate and mitigate risks
before they materialize. This predictive capability is
especially valuable in an era of increased uncertainty.
Limitations and Challenges
While the ANN-AHP integration offers several
advantages, it also has some limitations and challenges
that must be considered:
1.
Data Quality and Availability: The effectiveness
of the ANN component depends on the quality and
availability of data. Incomplete or inaccurate data can
lead to poor predictions and undermine the model’s
usefulness. Banks must ensure that they have access to
reliable, high-quality data in order for the ANN to make
accurate predictions.
2.
Subjectivity in AHP: While AHP provides a
structured method for prioritizing criteria, the process
still involves subjective judgments by experts. These
judgments can introduce bias, and the consistency of
the pairwise comparisons may vary among different
decision-makers. A potential solution to this issue is to
use multiple experts and aggregate their judgments, or
to apply techniques such as consistency checks to
ensure the validity of the AHP results.
3.
Complexity of Model Training: Training an ANN
model requires significant computational resources and
expertise in machine learning. The model must be
carefully designed, trained, and validated to ensure its
accuracy. In addition, the training process can be time-
consuming, especially if the dataset is large and
complex. Banks may need to invest in specialized
resources or collaborate with experts in machine
learning to build and maintain an effective ANN model.
4.
Interpretability of ANN: One of the challenges of
using ANN in decision-
making is that the model’s
predictions may not always be easily interpretable. ANN
is often referred to as a "black-box" model because it
can be difficult to understand exactly how the model
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arrived at a particular prediction. This lack of
transparency could be a concern for decision-makers
who need to justify their strategic choices to
stakeholders or regulatory authorities. To address this
issue, banks may need to combine ANN with other
methods, such as decision trees or explainable AI
techniques, to enhance the interpretability of the
results.
Future Directions
Despite these limitations, the integration of ANN and
AHP provides a promising approach to business
strategy estimation in the banking sector. Future
research could focus on improving the hybrid model by
incorporating additional decision-making methods or
refining the ANN model to improve its predictive
accuracy. For example, incorporating fuzzy logic into
the ANN-AHP model could help handle the uncertainty
and imprecision often present in strategic decision-
making. Additionally, advances in explainable AI could
help make ANN predictions more transparent and
interpretable, further enhancing their practical
application in strategic decision-making.
Furthermore, future studies could apply this integrated
model to other industries, such as insurance or
investment management, where similar challenges in
decision-making and strategy formulation exist. This
could lead to the development of a more generalized
framework for integrating machine learning with
multi-criteria decision-making techniques.
In conclusion, the integration of Artificial Neural
Networks (ANN) and Analytical Hierarchy Process
(AHP) presents a robust and innovative tool for
estimating business strategy effectiveness in the
banking sector. This hybrid model combines the
structured decision-making approach of AHP with the
predictive
capabilities
of
ANN,
offering
a
comprehensive solution for evaluating different
business strategies. While the model demonstrates
clear advantages in terms of data-driven decision-
making, holistic strategy formulation, and risk
management, it also faces challenges such as data
quality and model interpretability. Nevertheless, with
continued research and refinement, the ANN-AHP
integration has the potential to significantly enhance
strategic decision-making in the banking industry,
providing a competitive advantage in an increasingly
complex and dynamic market environment.
CONCLUSION
This study demonstrates the value of integrating
Artificial Neural Networks and the Analytical Hierarchy
Process as a tool for estimating business strategy
effectiveness in banks. By combining the structural
decision-making capabilities of AHP with the predictive
power of ANN, the hybrid model provides a
comprehensive approach for strategic decision-making
in the banking sector. As the banking industry continues
to face new challenges and opportunities, such
integrated models can support managers in making
more informed and effective strategic choices.
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