Enterprise Financial Fraud Early Warning and Risk Assessment
Model Based on Machine Learning Algorithm
Xin Yao and Juan Xu
Shandong Institute of Commerce and Technology, Jinan, 250000 Shandong, China
Keywords: Statistical Theory, Machine Learning Algorithms, Fraud Warning, Risk Assessment Model, Corporate
Finance.
Abstract: Fraud early warning and risk assessment models play an important role in corporate finance, but there is the
problem of inaccurate risk positioning. The traditional genetic algorithm cannot solve the problem of early
warning evaluation in enterprise finance, and the effect is not satisfactory. In an increasingly complex business
environment, businesses face increasing financial risk, with financial fraud being particularly devastating. As
technology advances, machine learning algorithms have become a powerful tool for improving businesses'
ability to identify potential financial fraud and conduct effective risk assessments. This article will explore
the application of machine learning in financial alerting and risk assessment, and highlight its importance in
maintaining healthy business operations.
1 INTRODUCTION
Machine learning algorithms train models by
analyzing large amounts of historical data, allowing
them to identify patterns and anomalies in the data. In
the financial realm, this means that algorithms can
learn to identify normal financial activity versus
abnormal activity that may point to fraud (Zhao and
Wang, et al. 2022). This capability is essential for
early detection of potential financial problems, as it
provides a window of time for businesses to take
action to prevent further losses.
2 RELATED CONCEPTS
2.1 Mathematical Description of a
Machine Learning Algorithm
For example, if a business has a sudden increase in
spending or trading patterns that don't meet industry
standards, machine learning models can flag these
anomalous activities as potential risk points (Sun and
Liu, et al. 2022). By monitoring and analyzing
transaction data in real-time, machine learning can
help businesses respond quickly to these warning
signs and conduct further investigations (Shao and
Huidan, 2022).
(1)
In addition, machine learning algorithms can be
used to build comprehensive financial risk
assessment models. .
(2)
These models can integrate multiple data sources,
including financial statements, market trends,
macroeconomic indicators, and more, to provide
insight into the overall health of the business (Mao
and Shi, et al. 2022). Through the comprehensive
analysis of this data, machine learning models can
help businesses predict their future financial situation
and assess the potential risks of different strategies.
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Yao, X. and Xu, J.
Enterprise Financial Fraud Early Warning and Risk Assessment Model Based on Machine Learning Algorithm.
DOI: 10.5220/0013543800004664
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 373-378
ISBN: 978-989-758-763-4
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
373

2.2 Selection of Fraud Early Warning
and Risk Assessment Model
Schemes
It's important to note that the effectiveness of a
machine learning model is largely dependent on the
quality and quantity of data used. Therefore,
businesses need to ensure that there is an accurate,
complete, and updated data set so that models can
learn and predict effectively (Wang and Xue, et al.
2023). At the same time, the design and training of
the model requires the cooperation of professional
data scientists and financial experts to ensure the
accuracy and usefulness of the model.
(4)
In practice, companies that have adopted machine
learning algorithms have seen significant results..
(5)
Not only do these algorithms improve the speed
and accuracy of fraud detection, but they also enhance
the overall understanding of risk for businesses. The
application of this technology allows companies to
manage their financial risks more proactively, rather
than just reacting to losses that have already occurred.
(6)
2.3 Analysis of Fraud Early Warning
and Risk Assessment Model
Schemes
In summary, machine learning algorithms are playing
an increasingly important role in corporate financial
fraud warning and risk assessment. By harnessing the
analytical power of these algorithms, businesses can
better identify potential risk points, take preventive
measures, and make more informed financial
decisions (Wang and Zhu, et al. 2022). As technology
continues to advance and more data becomes
available, we can foresee that machine learning will
continue to play a key role in protecting businesses
from the threat of financial fraud. .
(7)
Among them, it is In the
digital age, businesses face unprecedented data
growth and complexity. This environment creates a
hidden space for financial misconduct, making
traditional regulatory approaches inadequate to deal
with increasingly sophisticated financial fraud.
(8)
Therefore, the use of machine learning algorithms
for financial fraud early warning has become one of
the key technologies to improve enterprise risk
management capabilities (Qi and Xu, et al. 2023).
This article will explore how machine learning
algorithms can be effectively used to identify
potential financial risks and provide a strong early
warning mechanism for businesses
shown in Equation (9).
(9)
Financial fraud not only causes significant
economic losses to the enterprises themselves, but
also undermines market order and damages investor
confidence (Li and Hu, et al. 2022). With the
development of technology, machine learning has
gradually become a powerful tool for preventing and
detecting financial fraud due to its excellent data
processing ability and pattern recognition capabilities
(Jiang and Li, et al. 2022). By learning and analyzing
large amounts of historical data, machine learning
algorithms can reveal abnormal transaction behaviors
and potential risk points, so as to warn companies to
take measures to prevent fraud in advance
.
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INCOFT 2025 - International Conference on Futuristic Technology
374

(10)
The core strength of machine learning is its ability
to learn and adapt itself. By training models to
identify normal and abnormal patterns of financial
behavior, machine learning is able to continuously
optimize the accuracy of forecasts (Xu and Yang, et
al. 2022). This means that as more and more data is
analyzed, the predictive power of the algorithm will
gradually increase. In addition, machine learning
algorithms can process high-dimensional datasets,
which is difficult to achieve with traditional manual
methods.
3 OPTIMIZATION STRATEGY
OF FRAUD EARLY WARNING
AND RISK ASSESSMENT
MODEL
In practice, machine learning techniques such as
random forests, neural networks, and support vector
machines have been used to build early warning
systems. These systems analyze financial statements,
transactions, and other relevant data to identify
anomalous indicators such as abnormal cash flows,
inconsistent account movements, or unusual financial
ratios. When these abnormal signals are detected, the
system can issue a warning in time, prompting further
investigation by the relevant departments.
4 PRACTICAL EXAMPLES OF
FRAUD EARLY WARNING AND
RISK ASSESSMENT MODELS
4.1 Introduction to the Fraud Early
Warning and Risk Assessment
Model
Many studies have confirmed the effectiveness of
machine learning in financial fraud identification. For
example, some banks have used machine learning
algorithms to monitor credit card transactions, which
has led to a reduction in the incidence of fraud. In
another case, a large retailer significantly reduced
inventory theft incidents by applying anomaly
detection algorithms. These examples show that the
use of advanced data analytics technology can greatly
improve the risk management and prevention
capabilities of enterprises.
The fraud early warning and risk assessment
model process in Table I. is shown in Figure I.
Table 1: Fraud early warning and risk assessment model
requirements
Scope of
application
Grade Accuracy Fraud early
warning and
risk assessment
model
Prevent
financial
frau
d
I 85.00 78.86
II 81.97 78.45
Manage risk I 83.81 81.31
II 83.34 78.19
Assist in
decision-
makin
g
I 79.56 81.99
II 79.10 80.11
Figure 1: The analysis process of fraud early warning and
risk assessment models
While machine learning has made significant
progress in financial fraud early warning, challenges
remain. The quality and completeness of the data
directly affects the performance of the model, and the
data in the real world is often missing or noisy. In
addition, as financial fraud methods continue to
evolve, algorithms need to be constantly updated to
adapt to new situations. In the future, the combination
of other areas of AI, such as natural language
processing and reinforcement learning, will further
enhance the effectiveness of early warning systems.
4.2 Fraud Early Warning and Risk
Assessment Model
Overall, machine learning algorithms provide
businesses with a powerful tool for early warning of
financial fraud. By in-depth analysis and learning
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Enterprise Financial Fraud Early Warning and Risk Assessment Model Based on Machine Learning Algorithm
375

from historical data, they can efficiently identify
potential financial risks and anomalous behavior.
However, to reach its full potential, organizations
need to invest resources to maintain data quality and
ensure that models are continuously updated and
improved. In the face of the ongoing threat of
financial fraud, embracing machine learning is a
critical step in building a corporate line of defense.
Table 2: Overall picture of the fraud early warning and risk
assessment model scheme
Category Random
data
Reliability Analysis
rate
Prevent
financial
frau
d
85.32 85.90 83.95
Manage
ris
k
86.36 82.51 84.29
Assist in
decision-
making
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 Fraud Early Warning and Risk
Assessment Model and Stability
In this data-driven era, machine learning algorithms
have become an important weapon for businesses to
prevent financial fraud. By intelligently analyzing
massive amounts of data, machine learning can help
not only discover known risk patterns, but also predict
and identify new risk trends. While challenges
remain, the development of machine learning has
undoubtedly provided a more secure and transparent
financial environment for businesses.
Figure 2: Fraud early warning and risk assessment models
with different algorithms
As technology continues to advance, there is
reason to believe that the future of financial risk
management will be smarter and more efficient, and
machine learning will play a crucial role in this
transformation.
Table 3: Comparison of fraud early warning and risk
assessment model accuracy of different methods
Algorith
m
Surve
y data
Fraud
early
warning
and risk
assessmen
t model
Magnitud
e of
change
Error
Machine
learning
algorithm
s
85.33 85.15 82.88 84.9
5
Genetic
algorith
m
85.20 83.41 86.01 85.7
5
P 87.17 87.62 84.48 86.9
7
In today's digital age, the financial management of
enterprises has become increasingly complex. This
has been followed by an increase in financial fraud,
which not only threatens the survival and
development of enterprises, but also has a serious
impact on the stability of the entire market.
Figure 3: Fraud early warning and risk assessment model
for machine learning algorithms
A machine learning algorithm is a model trained
on a large amount of data that can predict the
occurrence of future events by learning patterns and
patterns in historical data. This feature makes
machine learning algorithms excellent at detecting
anomalous behavior.
INCOFT 2025 - International Conference on Futuristic Technology
376

4.4 Reasonableness of Fraud Early
Warning and Risk Assessment
Models
For example, when a business's revenue suddenly
spikes or costs are abnormally low, it can be a sign of
revenue manipulation, while a large long-term
difference between cash flow and net profit can mean
that there is a risk of inflating assets. Machine
learning algorithms can analyze historical data to
build a financial model of the company's normal
operations, and monitor the deviation of actual data
from it in real time.
Figure 4: Fraud early warning and risk assessment models
with different algorithms
Once a major deviation is found, the system will
automatically issue a warning to help the enterprise
identify the problem in time and take action.
4.5 Effectiveness of Fraud Early
Warning and Risk Assessment
Models
Of course, the application of machine learning
algorithms is not without its challenges. First, it
requires a lot of clean, accurate data as a foundation.
If the data quality is poor, the performance of the
algorithm will also be greatly reduced. Second, the
design and tuning of algorithms requires a certain
amount of expertise, which may require companies to
invest corresponding human resources. Finally,
although algorithms can provide a high degree of
automation and accuracy, the final decision still
requires human judgment. Therefore, when applying
machine learning algorithms, enterprises should
combine the opinions of professionals to ensure the
correctness of decision-making.
Figure 5: Fraud early warning and risk assessment models
with different algorithms
In corporate finance, any transaction or statement
item that deviates from the norm can be an indication
of potential fraud. Machine learning algorithms can
pinpoint these anomalous indicators so they can alert
managers to take action in a timely manner.
Table 4: Comparison of the effectiveness of fraud early
warning and risk assessment models of different methods
Algorith
m
Surve
y data
Fraud
early
warning
and risk
assessmen
t model
Magnitud
e of
change
Error
Machine
learning
algorithm
s
82.21 85.92 84.59 82.8
5
Genetic
algorith
m
83.73 84.23 84.41 83.5
5
P 84.20 87.39 84.76 83.9
0
In addition, another advantage of machine
learning algorithms is their ability to learn and adapt
on their own. With the passage of time and the
accumulation of data, the algorithm will continue to
optimize its model and improve the accuracy of
detection. This means that machine learning
algorithms remain highly effective at detecting even
in the face of evolving fraud methods.
Enterprise Financial Fraud Early Warning and Risk Assessment Model Based on Machine Learning Algorithm
377

Figure 6: Machine learning algorithm, fraud early warning
and risk assessment model
Therefore, how to effectively identify and prevent
financial fraud has become a problem that cannot be
ignored in enterprise management. Fortunately, with
the advancement of technology, machine learning
algorithms provide a completely new solution for
enterprises.
5 CONCLUSIONS
In conclusion, machine learning algorithms show
great potential in the management of corporate
financial fraud risk. Not only does it help companies
detect anomalies in a timely manner, but it also
improves their performance over time. In the digital
age, the use of machine learning algorithms to prevent
financial fraud has become an important tool for
enterprise risk management. As technology continues
to advance, there is reason to believe that machine
learning will play an even more critical role in the
future of financial management. Enterprises should
keep their finger on the pulse of the times and actively
introduce and apply machine learning algorithms to
build a safer and more stable financial environment..
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