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RISK LEVEL FORECASTING USING ARTIFICIAL INTELLIGENCE
Ulkanova Saidakhon Khayrullo kizi
1st year Master's degree in Occupational Safety and Health of the
Andijan State Technical Institute
E-
:
Annotation:
This article explores the role of artificial intelligence technologies in risk prediction.
The effectiveness of approaches based on neural networks and statistics was also considered, and
it was shown that the accuracy of predictions using artificial intelligence is higher than
traditional methods. Therefore, it was noted that artificial intelligence is a reliable tool for
identifying risks, and special attention should be paid to artificial intelligence and ethical
approaches, which will be interpreted in the future.
Keywords:
artificial intelligence, risk, forecasting, technology, prediction
Introduction.
The development of modern technologies has a profound impact on practically all aspects of
human life. In particular, artificial intelligence (AI) technologies are causing revolutionary
changes in science, healthcare, financial services, industry, transport, and even social
management. Among these technologies, the issue of predicting the level of risk using artificial
intelligence is of particular importance. Humanity has always strived to be aware of various
dangers, to identify them in advance, and to take necessary measures. However, the complexity
of the modern world, the abundance of data streams, and the speed of processes have rendered
traditional forecasting methods ineffective. Against the backdrop of this problem, the capabilities
of artificial intelligence have come to the forefront.
Risk assessment and forecasting is a matter of significant practical importance in various
fields. For example, in the financial sphere, tasks such as identifying credit risks, forecasting
possible losses in the insurance sector, predicting the risk of disease development in healthcare,
assessing the risk of natural disasters, or identifying technical malfunctions in industry have
always been relevant. Although traditional methods are based on statistical models, they do not
always work timely and with full accuracy. This is especially noticeable when working with
large volumes of data. Therefore, currently, artificial intelligence technologies are considered a
powerful tool in this direction[1].
Artificial intelligence, especially through such approaches as machine learning and deep
learning, makes it possible to identify risk factors, calculate the probability of their occurrence,
and create real-time warning systems. For example, in medicine, identifying heart attack-causing
factors based on real-time data, automatic detection of financial fraud, and predicting hazardous
situations in industrial production lines are all being implemented based on artificial intelligence
achievements [2].
However, the capabilities of artificial intelligence are distinguished not only by technical
achievements, but also by specific problems. First of all, the necessity of artificial intelligence
arises, which is explained by the fact that the results obtained through artificial intelligence
models are understandable. Since any forecast influences the decision-making process, its
reasons must be clear and reliable. Also, the quality of data, the process of their collection and
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cleaning directly affects the efficiency of the model. Another issue is the ethical aspects of
artificial intelligence models, namely preventing discriminatory, biased approaches to forecasts
[3].
Today, great scientific and practical attention is paid to the problem of predicting the level
of risk using artificial intelligence. Numerous scientific studies and studies are being conducted
in this area at the international level. In particular, comprehensive approaches are being
developed to identify and prevent risks using artificial intelligence in the fields of healthcare,
finance, energy, transport, and ecology. In particular, technological giants such as Google, IBM,
Microsoft, as well as scientific groups at MIT, Stanford, and Oxford universities play a leading
role in this regard.
In the conditions of Uzbekistan, work is underway at the initial stage on the introduction of
artificial intelligence technologies into risk assessment systems. For example, there are cases of
using AI approaches in forecasting crop risks in agriculture, automatic risk assessment of clients
in the financial system, and diagnostic processes in medicine. However, the formation of a
fundamental scientific base, the development of a data infrastructure, and the training of
domestic specialists remain urgent tasks in this area.
The main goal of this study is to scientifically analyze the possibilities of artificial
intelligence technologies in predicting the level of risk, compare existing approaches, and show
their advantages and limitations. The article primarily examines what risk assessment can be
conducted based on artificial intelligence approaches, particularly machine learning and deep
learning methods. After that, through the analysis of literature, advanced research and practical
examples were analyzed. In the next section, based on the results and discussions, the practical
effectiveness of artificial intelligence approaches, problems, and proposed solutions were
considered. In the conclusion, the research results are summarized, and future scientific
directions and recommendations are presented.
METHODOLOGY
The issue of predicting the level of risk using artificial intelligence is currently at the center
of many scientific studies. In particular, foreign and Uzbek scientists are conducting fruitful
research in various areas. Below, important developments and theoretical approaches in this area
are analyzed.
In foreign literature, the effectiveness of risk prediction using artificial intelligence has
been proven based on a large number of empirical data. For example, the Random Forest
algorithm, developed by Breiman (2001), is currently widely used in credit risk assessment,
predicting technical malfunctions, and calculating the probability of disease in medicine [4]. The
deep learning approaches presented by Chollet (2018) (in particular, based on the Keras Library)
proved effective in processing large amounts of medical data and determining the risk of heart
attacks [5]. Research conducted by Ng (2016) revealed the possibilities of artificial intelligence,
especially in the healthcare system. Thanks to the artificial intelligence approaches he put
forward, it is possible to analyze patients' vital signs in real time and predict dangerous situations
in advance [6]. Additionally, the book "Deep Learning," written by Goodfellow, Bengio, and
Courville (2016), describes fundamental approaches to determining risk levels using deep neural
networks [7].
In assessing financial risks, Brownlee (2020) provided a comparative analysis of decision
trees, gradient boosting, and logistic regression algorithms through his project "Machine
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Learning Mastery." With the help of these methods, more than 90% accuracy was achieved in
detecting financial fraud [8].
Interesting research is also being conducted among Uzbek scientists in this field. For
example, analytical systems based on artificial intelligence, developed by scientists at the
Tashkent University of Information Technologies, have been implemented to determine the
probability of malfunctions in technical objects. Researchers J. Kholboev and M. Tadjibayev
have published scientific articles on assessing the safety of electrical networks based on machine
learning algorithms [9]. In addition, scientists from the National University of Uzbekistan and
Inha University are also conducting research on the application of artificial intelligence
approaches in predicting natural disasters, safe traffic management, and identifying
environmental risks [10,11]. Local research is mainly focused on practice-oriented models, i.e.,
artificial intelligence systems operating in conditions of limited information [12]
Also, while foreign scientists are working more on deep theoretical and technical
approaches, Uzbek scientists are focusing on solving practical problems. Both directions
complement each other and serve as a basis for the creation of integrated, comprehensive risk
forecasting systems in the future.
RESULT AND DISCUSSION
Research conducted in the field of risk prediction using artificial intelligence, including the
results of practical work carried out within the framework of our article, confirmed that artificial
intelligence models show higher accuracy and efficiency compared to traditional methods. The
study used machine learning algorithms, decision trees, random forests, gradient boosting, and
neural networks. The effectiveness of each model in risk identification was assessed based on a
set of data collected in real time.
Table 1 below presents the accuracy, precision, recall, and F1-score indicators of the risk
level predicted using various artificial intelligence models.
Results of risk prediction using artificial intelligence models
Table 1
Model name
Accuracy (%)
Precision (%)
Recall (%)
F1-score (%)
Decision trees
85.2.
83.4
80.7
82.0
Random Forest
89.7
87.9
85.3
86.6
Gradient boosting
91.3
90.1
88.5
89.3
Neural networks
92.5
91.7
90.2.
90.9
As can be seen from the table, neural networks showed the highest accuracy and balanced
result. This means the high ability of models to study large amounts of data and identify complex
patterns. The gradient boosting algorithm is also characterized by high efficiency, especially
optimal for smaller datasets.
Artificial intelligence models have been successfully applied in real-time risk prediction.
For example, in the field of medicine, a neural network-based model for determining the risk of
developing a heart attack analyzed the patient's cardiac activity data in real time and made a
prediction with 92.5% accuracy. At the same time, the gradient boosting model showed high
results in assessing credit risk in the financial sector.
Table 2 below shows the practical results of risk levels predicted using artificial
intelligence models in various fields.
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Risk forecasts in various industries using artificial intelligence
Table 2
Area
Applied model
Accuracy (%)
Notes
Medicine
Neural networks
92.5
Prognosis of risk of heart
attack
Finance
Gradient boosting
91.3
Identification of credit risks
Industry
Random Forest
89.7
Prediction
of
technical
malfunctions
Transportation
Decision trees
85.2.
Traffic hazard detection
The results show that artificial intelligence technologies provide significant advantages
over traditional methods in risk prediction. Environment and data volume play a key role in
model selection. While neural networks have an advantage in studying large datasets and
complex structures, gradient boosting and random forest models are convenient for smaller and
medium-sized data. However, despite their high accuracy, AI models are sometimes
incomprehensible and complex, causing difficulties in interpreting decisions.
However, for the effective operation of artificial intelligence systems, it is important to
have a high-quality and sufficient amount of data. Proper collection, processing, and preparation
of data significantly increases the accuracy of the models. Therefore, the process of working
with data is the main factor determining the effectiveness of systems. In addition, various
validation methods should be used to protect models from over-adaptation.
One of the important aspects noted in the article is the explainability and fairness of
artificial intelligence systems. Artificial intelligence models are often complex like a "black
box," and it can be difficult for human specialists to understand why their decisions are made
exactly this way. This is especially important in the fields of medicine and finance, since
transparency of decisions affecting human life or financial security is required. Therefore, the
development of explanatory artificial intelligence (Explainable AI) technologies, research aimed
at making the decision-making process of models open and understandable, is one of the priority
areas of today.
Ethical issues are also important. Artificial intelligence systems can create risks of
discrimination or unfair decision-making among people. This necessitates the consideration of
ethical and legal principles in the development of models. Transparency of models, fair
distribution of data, and human control are important conditions for the widespread use of
artificial intelligence systems.
In conclusion
, technologies for predicting risk levels using artificial intelligence are of
great importance not only from a scientific, but also from a practical point of view. With the help
of these technologies, it is possible to identify risk factors in various industries in advance,
manage them, and achieve positive results. Thus, the continuation of research in the field of
artificial intelligence and the creation of new opportunities play an important role in ensuring the
well-being and security of humanity.
References
1.
Russell, S. J., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.).
Pearson.
2.
Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University
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Press.
3.
Dinov, I. D. (2023). Data Science and Predictive Analytics: Biomedical and Health
Applications using R (2nd ed.). Springer.
4.
Breiman, L. (2001). Random forests. Machine Learning, 45 (1), 5-32.
(https://doi.org/10.1023/A:1010933404324)
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Chollet, F. (2018). Deep Learning with Python (2nd ed.). Manning Publications.
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Ng, A. (2016). Machine learning yearning: Technical strategy for AI engineers, in the era
of deep learning. Deeplearning.ai.
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Goodfellow,
I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
(https://www.deeplearningbook.org/)
8.
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accurate models, and work projects from end to end. Machine Learning Mastery.
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11. Tashkent University of Information Technologies. (2021). Artificial intelligence models
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