CC BY
4УДК 33
Rakhimzade K.
Digital Transformation Senior Manager, PepsiCo Holdings (Moscow, Russia)
INTEGRATING MACHINE LEARNING INTO RISK MANAGEMENT PROCESSES TO ENHANCE DECISION-MAKING WITHIN RISK MANAGEMENT
Abstract: against the background of increasing uncertainty and complexity of the business environment, the usual risk management methods often turn out to be insufficiently effective. Traditional approaches are being replaced by innovative ones. The article is devoted to the urgent problem of integrating machine learning technologies into risk management processes to improve the quality of decisions. The purpose of the study is to analyze the potential and the most obvious limitations of the application of appropriate methods in various management aspects. Contradictions are found between the need to improve the accuracy of risk forecasting and the barriers associated with the interpretability of machine learning models, as well as between the need to process large amounts of data and the requirements for protecting confidential information.
The systematization of the advantages of the application is presented, as well as the author's view on the allocation of "problem areas" when using machine learning tools in risk management is presented. The article pays special attention to promising areas of development — in particular, it implies federated learning, explicable artificial intelligence, etc. This work and its results are of interest to specialists in risk management, and data science, and managers interested in improving the effectiveness of risk management in their organizations.
Keywords: machine learning, decision support, predictive analytics, risk management, digital transformation.
Introduction.
In the context of modernity, characterized by a high degrof uncertainty and a dynamic entrepreneurial environment, effective risk management has become a determining factor for the success of economic entities. Traditional methods of analysis
and evaluation often fail to fully address the growing volume of data and the complexity of interrelationships between various risk factors. In this regard, the integration of machine learning (ML) into management processes opens up new possibilities for improving forecasting accuracy and decision-making support. The research problem lies in the need to develop and implement innovative approaches to risk management that would enhance forecasting accuracy and the quality of managerial decisions. From this perspective, the integration of machine learning methods into risk management appears to be a promising direction, requiring detailed exploration both in terms of potential benefits and the challenges and limitations it presents.
Methods and Materials.
The article utilizes comparative analysis, systematization, study of contemporary literature, and generalization. A review of scientific works revealed the multifaceted approaches to the use of ML technologies in various aspects of risk management.
Particular attention is paid to the application of machine learning in the financial sector. P.A. Adamenko, V.Y. Cibulnikova, and I.P. Nuzhina explore its potential for managing entrepreneurial risks in small businesses [1]. In an international context, D. Kumar and Sh. Singh analyzes the impact of machine learning algorithms on fraud detection in institutions [7]. M. Mwangi examines the role of ML in enhancing management strategies [8], while S. Satwinder focuses on the nuances of applying this tool in financial services [9].
It is also worth mentioning the publication by In.Ju. Song and W. Heo, propose an innovative approach to predicting errors in insurance reserves using a combination of methods [10]. This demonstrates the potential of hybrid machine learning models in addressing complex risk management tasks.
In the field of personnel management, M.N. Vrazhnova and M.A. Filatov characterize the features of using ML in automated personnel management systems to predict personnel risks [3].
S.A. Makushkin, T.V. Ukhina, V.A. Sinyukov, and E.P. Kochetkov discuss the nuances of applying machine learning in risk management for engineering projects [4], showcasing the potential of these technologies in technical fields and project management.
T.V. Uvakina analyzes problematic aspects of applying ML technologies in the investment sector [6], highlighting the need for a critical approach to their implementation and an awareness of potential limitations.
P.N. Nestyagin explores key directions for integrating digital technologies into the economic processes of knowledge-intensive enterprises [5], enabling a broader view of machine learning applications within the context of business digitalization.
S.V. Valyak discusses new approaches to risk management in today's conditions [2], complementing the overall picture by emphasizing the importance of innovative thinking in risk management.
Thus, the authors draw on a wide range of methodological approaches, paying particular attention to the interpretability of ML models and their integration with existing business processes. In summary, there is a growing interest in the application of ML technologies in risk management.
Results and Discussion.
The conceptual foundations of machine learning are based on the idea of creating algorithms and statistical models that can autonomously improve their performance in solving specific tasks—relying on experience—without explicitly programming each step [2, 7].
The fundamental principle involves extracting patterns from large datasets and forming generalizations that help make predictions or decisions in new, previously unseen situations.
A key concept is the training set—a collection of examples on which the system learns to recognize patterns and build predictive models.
The learning process typically involves optimizing the model's parameters to minimize a loss function, which quantifies the discrepancy between the model's predictions and the actual values of the target variable.
The concept of generalization ability plays a critical role, referring to the model's capability to perform accurately on new, unseen data. To achieve this, various regularization methods are employed to prevent overfitting—a situation where the model fits the training data too closely, losing its ability to generalize.
An essential aspect is the selection of an appropriate data representation or feature space in which patterns are sought. Modern approaches (e.g., deep learning) enable the automatic formation of hierarchical representations, extracting increasingly abstract features as the model progresses through the layers of the neural network.
The application of ML algorithms helps overcome several limitations inherent in traditional risk management methods. Specifically, machine learning provides the following advantages (Fig. 1):
Fig. 1. Systematization of the advantages of using machine learning in risk management [1, 4, 10].
Given the effects noted above, it is advisable to focus on the key areas where machine learning (ML) is applied in risk management, which will be considered through the following examples:
- credit scoring and creditworthiness assessment,
- fraud detection,
- operational risk management,
- market risk management.
For instance, the methodological framework of machine learning (gradient boosting, neural networks) enables the creation of more accurate models for credit risk assessment. These algorithms can account for a wide range of factors, including borrowers' behavioral characteristics, significantly improving the prediction of default probability.
Similarly, the application of anomaly detection algorithms and classification techniques allows for the efficient detection of suspicious transactions in real time. Self-learning systems adapt to new fraud schemes, providing more reliable protection for financial institutions and their clients.
Clustering methods and time series analysis provide the ability to identify potential operational disruptions at an early stage. Predictive maintenance, based on ML, allows for the optimization of maintenance schedules, minimizing equipment downtime risks.
ML algorithms, particularly recurrent neural networks, have proven highly effective in forecasting the volatility of financial instruments and in modeling market development scenarios [6]. This facilitates the creation of resilient investment portfolios and the development of flexible hedging strategies.
Despite the clear advantages and numerous beneficial effects, the implementation of machine learning methods in risk management processes is associated with several challenges, which are systematized in Figure 2.
Fig. 2. Identification of "problem areas" when using machine learning in risk management [3, 8].
Commenting on the above scheme, it should be noted that the interpretability of models remains a pressing issue. Many machine learning algorithms, particularly deep neural networks, operate as a kind of "black box," making it difficult to explain the decisions made to regulators and other stakeholders.
The effectiveness of machine learning models is directly dependent on the quality of the initial data. Incomplete or biased training datasets inevitably lead to inaccurate conclusions.
The application of machine learning algorithms in certain areas (such as credit scoring) is often limited by legal requirements concerning personal data protection and anti-discrimination laws.
In addition to this, the development and maintenance of machine learning systems require specialists who possess expertise in both data analysis and risk management.
Finally, in a rapidly changing business environment, machine learning models can become outdated, necessitating continuous monitoring and regular updates.
For the successful integration of machine learning into management processes in this area, it is recommended to:
- Develop interpretable machine learning methods, such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (Shapley Additive exPlanations), which allow for the explanation of decisions made by complex models,
- Implement thorough validation and testing practices, including the evaluation of model robustness under different scenarios and stress testing,
- Develop hybrid approaches that combine expert knowledge with machine learning capabilities to enhance the reliability and interpretability of results,
- Invest in building employcompetencies and creating interdisciplinary teams that bring together experts in risk management, data science, and IT,
- Implement systems for continuous monitoring of model performance, along with mechanisms for automatic adjustments when conditions change.
As it appears, the further development of machine learning (ML) integration into risk management is associated with the following key vectors (Fig. 3):
Federated learning
Automated Machine Learning
Quantum Machine Learning
Explainable Artificial Intelligence (XAI)
Fig. 3. Promising vectors for the development of ML integration into risk management (compiled by the author).
Federated learning allows models to be trained on distributed data without centralizing it, which is particularly significant in the context of tightening personal data protection requirements.
Quantum machine learning has the potential to help solve complex optimization problems in risk management at unprecedented speeds.
Automated machine learning simplifies the process of model development and implementation, making ML technologies more accessible to risk management professionals.
In addition, the development of methods that ensure transparency and interpretability of models is crucial for enhancing trust in decision-support systems based on ML.
Conclusion.
The integration of machine learning into risk management processes represents a promising direction that has the potential to significantly enhance the effectiveness of risk management across various industries.
Despite the numerous and substantial challenges, the advantages of using ML in risk analysis and forecasting are undeniable. It appears that the key to the successful implementation of these technological developments lies in balancing innovative algorithmic approaches with traditional expert knowledge in the field of risk management.
As technologies advance and current limitations are addressed, the role of machine learning in supporting decision-making within risk management will continue to strengthen, unlocking further opportunities to improve the resilience and competitiveness of enterprises in an environment of global uncertainty.
СПИСОК ЛИТЕРАТУРЫ:
1. Adamenko P.A. The use of machine learning and artificial intelligence in managing entrepreneurial risks in small business / P.A. Adamenko, V.Y. Cibulnikova,
1.P. Nuzhina // Journal of Applied Research. - 2023. - No. 10. - pp. 66-72;
2. Valyak S.V. Innovative approaches to enterprise risk management in modern conditions / S.V. Valyak // Advances in Science and Technology. Collection of articles of the LXII International scientific and practical conference. - Moscow: 2024. - pp. 175-177;
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4. Makushkin S.A. Application of machine learning in enterprise risk management in the implementation of engineering projects / S.A. Makushkin, T.V. Ukhina, V.A. Sinyukov, E.P. Kochetkov // Innovations and investments. - 2024. - No. 1. - pp. 248252;
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7. Kumar D. Analyzing the impact of machine learning algorithms on risk management and fraud detection in financial institutions / D. Kumar, Sh. Singh // International Journal of Research Publication and Reviews. - 2024. - Vol. 5. - No. 5. - Pp. 1797-1804;
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