Financial Early Warning Model of Electric Power Enterprises Based
on Attribute Reduction Algorithm
Xiufeng Le, Dong Wang, Yongbing Yan, Shuai Zhang and Hongmei Zhang
Beijing China-Power Information Technology Co. Ltd., 102200, China
Keywords: Rough Theory, Property Reduction Algorithm, Financial Early Warning Model, Electricity, Corporate
Finance.
Abstract: Financial early warning model plays an important role in the finance of power enterprises, but there is the
problem of inaccurate forecasting. In data analysis, attribute reduction is a process of reducing the number of
features in a dataset with the aim of removing those attributes that have little impact on classification or
prediction results, thereby improving data processing efficiency and reducing computational costs, while
avoiding "dimensional disasters". Attribute reduction methods usually include feature selection and feature
extraction. In the realm of financial management within power companies, maintaining a robust system that
can accurately predict financial risks and pitfalls is paramount. One innovative approach that has gained
significant traction for improving the forecasting accuracy is the implementation of attribute reduction
algorithms. These algorithms are designed to simplify data sets by identifying and eliminating irrelevant or
redundant attributes, which can significantly enhance the effectiveness of financial early warning systems. In
this article, we will delve into the advantages, applications, and potential challenges associated with attribute
reduction algorithms in the context of power enterprises' financial risk forecasting.
1 INTRODUCTION
The power industry is a crucial component of any
modern economy (Zhong, 2011), providing the
necessary energy for businesses to operate (Zhang,
2022), homes to stay lit (Li and Lu, 2021), and society
to function effectively (Ma, Yang, et al. 2021).
However, as with any industry, financial stability and
sustainability are paramount concerns (He, 2022). In
recent years, the need for effective financial
forecasting (Zhang, 2022) and early warning systems
in electricity companies (Liu, 2021) has become
increasingly apparent (Li, 2021). This article will
explore the importance of comprehensive financial
forecasting for these companies (La, and Shao, 2023),
highlighting the key indicators and strategies that can
help prevent financial crises (Dong, 2021) and ensure
long-term profitability (Lin, Li, et al. 2021).
2 RELATED CONCEPTS
2.1 Mathematical Description of the
Attribute Reduction Algorithm
To begin with, it is essential to understand the
significance of accurate financial projections for
electricity companies. These organizations are
heavily reliant on capital-intensive projects and
investments, making them vulnerable to sudden
changes in market conditions or unforeseen events
such as natural disasters. Accurate financial forecasts
enable companies to identify potential risks early on,
mitigate their impact, and make informed decisions
regarding future investments or divestments.
Moreover, sound financial planning can enhance
shareholder confidence, improve credit ratings, and
secure access to funding at favorable termsl, and the
calculation is shown in Equation (1).
lim( ) max( 2)
iij ij ij
x
yt y t
→∞
⋅=≥ ÷
(1
)
Le, X., Wang, D., Yan, Y., Zhang, S. and Zhang, H.
Financial Early Warning Model of Electric Power Enterprises Based on Attribute Reduction Algorithm.
DOI: 10.5220/0013535100004664
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 1, pages 45-50
ISBN: 978-989-758-763-4
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
45

Among them, the judgment of outliers is shown in
Equation (2).
2
max( ) ( 2 ) ( 4)
ij ij ij ij
tttmeant=∂ + ⋅ +

 M
(2)
In creating an effective financial forecasting
system, electricity companies should focus on several
core components. Firstly, it is vital to establish a
robust set of financial metrics that accurately capture
the organization's financial health. Key performance
indicators (KPIs) such as cash flow, debt-to-equity
ratio, return on assets (ROA), and operating margin
should be regularly monitored and analyzed. By
doing so, companies can gain a clear understanding
of their financial status and identify areas for
improvement or concern.
𝐹(𝑑

)=ℝ 𝑡


𝜉⋅
√
2 →

𝑦

⋅7
(3
)
2.2 Selection of Financial Early
Warning Model Scheme
This collaborative approach not only enhances the
accuracy of forecasts but also strengthens team
morale and commitment to the organization's long-
term success.
()= ( )
ii i i
dy
g
txz Fd w
dx
⋅−
∏

(4
)
Based on Assumptions I and II, the comprehensive
function of the financial early warning model can be
obtained, as shown in Equation (5).
lim ( ) ( ) max( )
ii ij
x
gt Fd t
→∞
+≤

(5
)
It is crucial to regularly review and update financial
forecasts as new data becomes available or market
conditions change. This ongoing process allows
companies to adapt to evolving circumstances, refine
their predictions, and maintain a competitive edge in
the industry
𝑔(𝑡

)+𝐹(𝑑

)

↔𝑚𝑒𝑎𝑛(𝑡

+4)
(6
)
2.3 Analysis of Financial Early
Warning Model Scenarios
Ensuring proper communication and collaboration
between different departments within the
organization is critical. Finance teams must work
closely with operational, sales, and marketing
departments to gather information on upcoming
projects, market developments, and customer
behavior. By combining this information with
financial data, more holistic and accurate forecasts
can be produced.
𝑁𝑜(𝑡

)=
𝑔(𝑡

)+𝐹(𝑑

)

𝑚𝑒𝑎𝑛(
∑
𝑡


+4)

𝑏

−4𝑎𝑐
(7
)
Fostering a culture of transparency and
accountability is essential for successful financial
forecasting. Senior management should encourage
open dialogue about potential risks and challenges,
allowing employees to contribute their insights and
ideas.

() [ () ( )]
iii
Z
ht gt F d=+

(8
)
Secondly, adopting a forward-looking perspective
is crucial when developing financial forecasts. This
involves analyzing historical data, current trends, and
macroeconomic factors that may affect the industry.
It is also essential to consider potential scenarios,
including both positive outcomes and potential risks,
allowing the organization to prepare for various
possibilities. Additionally, implementing scenario
analysis techniques, such as Monte Carlo simulations,
can further enhance the accuracy of predictions by
accounting for the variability and uncertainty inherent
in financial data.
𝑎𝑐𝑐𝑢𝑟(𝑡

)=
𝑚𝑖𝑛[
∑
𝑔(𝑡

)+𝐹(𝑑

)

]
∑
𝑔(𝑡

)+𝐹(𝑑

)

× 100%
(9
)
Thirdly, incorporating advanced technology and
analytical tools can significantly improve the
effectiveness of financial forecasting. Artificial
intelligence (AI) and machine learning algorithms
can analyze vast amounts of data more efficiently
than traditional methods, identifying patterns and
predicting future trends with greater accuracy.
Moreover, utilizing cloud-based platforms enables
real-time data collection and sharing, allowing for
more timely decision-making and risk management.
𝑎𝑐𝑐𝑢𝑟(𝑡

)=
𝑚𝑖𝑛[
∑
𝑔(𝑡

)+𝐹(𝑑

)

]
∑
𝑔(𝑡

)+𝐹(𝑑

)

+ 𝑟𝑎𝑛𝑑𝑜𝑛(𝑡

)
(10
)
Another consideration is that of balancing data
reduction with information loss. While the goal is to
eliminate redundant or less informative attributes,
doing so must not sacrifice valuable insights. Striking
INCOFT 2025 - International Conference on Futuristic Technology
46

this balance requires a nuanced approach, combining
domain expertise with data mining expertise to ensure
that the reduced data set still captures the complexity
of financial dynamics within the power sector.
3 OPTIMIZATION STRATEGY
OF FINANCIAL EARLY
WA RN I N G M O D E L
Power enterprises are complex entities operating
within a dynamic environment, where numerous
factors such as fluctuating energy demands,
regulatory changes, and market fluctuations can
impact their financial stability. Financial risk
management, particularly the capability to anticipate
and prepare for potential financial distress, is critical
for these companies to sustain their operations and
growth. The traditional methods of financial analysis
often involve extensive data collection, including
both financial and non-financial variables. However,
not all collected data are equally informative or
relevant, which can lead to complications in
predictive models and diminish their accuracy. This
is where attribute reduction techniques step in,
offering a pathway to streamline data and focus on the
most pertinent indicators for financial health.
4 PRACTICAL EXAMPLES OF
FINANCIAL EARLY WARNING
MODELS
4.1 Introduction to the Financial Early
Warning Model
In addition, as with any model-based approach, the
effectiveness of attribute reduction algorithms can be
influenced by external factors such as changes in
market conditions, new regulations, or shifts in
consumer behavior. Therefore, it's essential for power
enterprises to regularly review and update their
financial early warning systems to reflect the current
state of affairs accurately, and the financial early
warning model scheme of the specific financial early
warning model is shown in Table 1.
The financial early warning model process in
table I, as shown in figure 1.
Table 1: Financial early warning model requirements.
Scope of
application
Grade Accuracy Financial
early warning
model
Profit
assessment
I 85.00 78.86
II 81.97 78.45
Fundraising
I 83.81 81.31
II 83.34 78.19
Risk
monitoring
I 79.56 81.99
II 79.10 80.11
Attribute
reduction
Analysis
Financing
Rough theory
Financial early
warning
Electric Company
Figure 1: The analytical process of the financial early
warning model.
Attribute reduction algorithms, drawing from the
fields of artificial intelligence and computational
intelligence, are methodologies that analyze large
datasets to discern patterns and relationships among
various data points. They employ techniques such as
rough set theory, discernibility matrix, and heuristic
algorithms to identify core variables that contribute
most significantly to the prediction of a target
outcome – in our case, the financial well-being of a
power enterprise. By stripping away superfluous data,
these algorithms not only improve the efficiency of
data processing but also enhance the clarity and
reliability of forecasting models.
4.2 Financial Early Warning Model
The application of these algorithms in financial early
warning systems within power companies offers
several benefits. Firstly, it enables more precise
identification of leading indicators of financial
distress. These might include metrics like cash flow
adequacy, debt-to-equity ratios, profitability trends,
and others that have been empirically demonstrated to
be strong predictors of financial health. Secondly, the
simplified data structure allows analysts to more
easily visualize the interrelations between different
indicators, providing a clearer understanding of the
underlying drivers of financial performance. Thirdly,
Financial Early Warning Model of Electric Power Enterprises Based on Attribute Reduction Algorithm
47

by reducing noise in the data, attribute reduction can
minimize false positives or negatives in predictions,
thereby increasing the trustworthiness of the warning
system, and the scheme of financial early warning
model is shown in Table 2.
Table 2: Overall status of the financial early warning model
scenario.
Category Random
data
Reliability Analysis
rate
Profit
assessment
85.32 85.90 83.95
Fundraisin
g
86.36 82.51 84.29
Risk
monitorin
g
84.16 84.92 83.68
mean 86.84 84.85 84.40
X6 83.04 86.03 84.32
P=1.249
4.3 Financial Early Warning Model
and Stability
Additionally, conducting periodic stress tests can help
identify potential vulnerabilities and inform
contingency planning efforts, and the financial early
warning model scheme is shown in Figure 2.
Figure 2: Financial early warning models with different
algorithms.
However, the integration of attribute reduction
techniques into financial forecasting models does
present certain challenges. One of the primary
concerns is the need for expert knowledge in both
finance and data analytics to select the appropriate
algorithm and interpret its outcomes correctly.
Moreover, the quality and integrity of the original
dataset are crucial; errors or inconsistencies in the
input data can adversely affect the results of the
algorithm, potentially leading to misleading
conclusions. The average financial early warning
model scheme of the above three algorithms is shown
in Table 3.
Table 3: Comparison of the accuracy of financial early
warning models of different methods.
Algorithm Survey
data
Financial
early
warning
model
Magnitude
of change
Error
Attribute
reduction
algorithm
85.33 85.15 82.88 84.95
Ant
colony
algorith
m
85.20 83.41 86.01 85.75
P 87.17 87.62 84.48 86.97
To maximize the value derived from attribute
reduction algorithms, power companies should also
consider integrating them into a comprehensive
decision support system. This integration would
allow for real-time monitoring of financial health
indicators and proactive intervention when early
signs of financial distress are detected. Additionally,
leveraging the insights from these algorithms can
guide strategic planning efforts, such as capital
allocation, risk mitigation strategies, and long-term
investment decisions, the attribute reduction
algorithm is generally analyzed by different methods,
Figure 3 shown.
Figure 3: Financial early warning model with attribute
reduction algorithm.
In addition, as with any model-based approach,
the effectiveness of attribute reduction algorithms can
be influenced by external factors such as changes in
market conditions, new regulations, or shifts in
consumer behavior. Therefore, it's essential for power
enterprises to regularly review and update their
financial early warning systems to reflect the current
state of affairs accurately.
INCOFT 2025 - International Conference on Futuristic Technology
48

4.4 Rationality of the Financial Early
Warning Model
Through this processing, it not only reduces the input
variables of the model and simplifies the subsequent
calculation process, but also helps to eliminate the
multicollinearity problem between variables, and
enhances the generalization ability and prediction
accuracy of the mode, and the financial early warning
model scheme is shown in Figure 4.
Figure 4: Financial early warning models with different
algorithms.
In conclusion, the adoption of attribute reduction
algorithms presents an exciting opportunity for power
enterprises to refine their financial risk forecasting
capabilities. By embracing this cutting-edge
technology, companies can create more accurate and
reliable early warning systems, which are crucial in
today's ever-changing economic landscape. As we
continue to navigate through a future defined by data
abundance, the ability to distill information down to
its most actionable form will undoubtedly give those
who master it a competitive edge, ensuring the
continued vitality and success of the power industry
for years to come.
4.5 Effectiveness of the Financial Early
Warning Model
Taking principal component analysis as an example,
we can combine many financial ratios to extract a few
principal components that are independent of each
other and represent the majority of the information,
he financial early warning model scheme is shown in
Figure V shown.
Figure 5: Financial early warning models with different
algorithms.
By focusing on robust financial metrics, adopting
a forward-looking perspective, leveraging advanced
technology, fostering cross-functional collaboration,
promoting a culture of transparency and
accountability, and maintaining regular reviews and
updates, companies can significantly enhance their
financial stability and resilience. With a
comprehensive approach to financial forecasting,
electricity companies can confidently navigate
through uncertain times and emerge stronger, more
profitable, and better prepared for future
challenges.The average financial early warning
model scheme of the above three algorithms is shown
in Table 4.
Table 4: Comparison of the effectiveness of financial early
warning models of different methods.
Algorithm Survey
data
Financial
early
warning
model
Magnitude
of change
Error
Attribute
reduction
al
g
orith
m
82.21 85.92 84.59 82.85
Ant
colony
algorith
m
83.73 84.23 84.41 83.55
P 84.20 87.39 84.76 83.90
In the realm of financial management within
power companies, maintaining a robust system that
can accurately predict financial risks and pitfalls is
paramount. One innovative approach that has gained
significant traction for improving the forecasting
accuracy is the implementation of attribute reduction
algorithms. These algorithms are designed to simplify
data sets by identifying and eliminating irrelevant or
Financial Early Warning Model of Electric Power Enterprises Based on Attribute Reduction Algorithm
49

redundant attributes, which can significantly enhance
the effectiveness of financial early warning systems.
In this article, we will delve into the advantages,
applications, and potential challenges associated with
attribute reduction algorithms in the context of power
enterprises' financial risk forecasting, Figure VI
shown.
Figure 6: Attribute reduction algorithm, financial early
warning model.
It is worth mentioning that although the attribute
reduction algorithm can greatly improve the
efficiency and accuracy of the model, it also has
certain limitations. For example, PCA assumes that
the data conform to a normal distribution and that the
principal components are independent of each other,
which is not always true in the actual complex and
volatile financial data. Therefore, in practice, we need
to combine a variety of algorithms and expert
experience to continuously optimize and adjust the
model.
5 CONCLUSIONS
In conclusion, by introducing the attribute reduction
algorithm, we can create an efficient and accurate
financial early warning model for power companies.
The model can not only help power managers and
investors identify potential financial problems early,
but also provide decision-making support for relevant
regulatory authorities, so as to maintain the stable
development of the entire industry. In the future, with
the continuous advancement of big data and artificial
intelligence technology, more innovative methods
and practical solutions will emerge in this field to
provide strong support for the risk management work
of power enterprises.
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