Artificial intelligence can find and is finding widespread application in almost every area of business. A prime example is AI-driven risk management. Anyone who adopts it achieves greater accuracy in risk assessment and more effective management strategies, gaining a significant competitive advantage. This is equally true for investors, whether they are large companies, funds, or individuals.
AI in Risk Management
Machine learning (ML) algorithms and artificial intelligence have long been used by major market players, such as hedge funds and banks, to build automatic and automated trading systems. For example, JPMorgan Chase employs about 1,000 data managers, 900 data processing specialists, 600 ML engineers, and a team of 200 in the AI research group. By analyzing 25 years of Federal Reserve speeches, the team concluded that AI can predict policy changes that can be used in trading securities. Currently, the bank has over 300 applications of neural networks. Another example is Bloomberg, which is actively developing and implementing artificial intelligence in finance and recently created a model with 50 billion analyzed parameters. Its AI, called BloombergGPT, is trained on market data to perform forecasting tasks in the financial sector.
According to some experts, financial risk assessment using AI is so in demand today that the market for it in the US alone is valued at $2.3 billion, with a projected growth to $7.4 billion by 2032. An average growth rate of over 16% highlights the substantial value AI brings to identifying and managing business risks.
What tasks do market professionals assign or are ready to assign to modern neural networks? The list is quite extensive.
Identification of New Risks
Risk identification is the process of discovering new types of risks and determining their main characteristics, causes, and development nature. For AI-based applications, the task involves detecting anomalies in historical data sets. Additionally, AI should identify:
- Objects and subjects involved in the formation of such anomalies.
- Mechanisms of interaction and relationships between them.
- Profile of the anomaly, including distribution characteristics, frequencies of occurrence, maximum and minimum deviations, etc.
This task is crucial, for instance, in portfolio investing (identifying unaccounted risks in portfolio strategies), detecting fraud in online payments, and other similar scenarios.
Risk Realization Probability Assessment
Risk analysis with AI must also answer the question: what is the probability that a risk considered during decision-making (e.g., when creating an investment portfolio) will actually materialize? As in the previous case, the primary dataset for machine learning is a historical data array, including information about the objects and subjects involved in forming this dataset at each point in time.
Artificial intelligence should identify the onset of a crisis (anomalous) situation, its causes, and estimate the likelihood of their occurrence based on the identified relationships and interactions. The example with JPMorgan Chase mentioned above addresses a similar task, although instead of assessing risk realization, it evaluates the probability of market inefficiencies that provide a competitive advantage.
Risk Situation Modeling
AI can generate synthetic historical data to test possible strategies, including automated risk management. This data flow can include not only situations similar to those that occurred in reality but also shock scenarios.
This allows for a more detailed risk assessment and the evaluation of automated systems’ ability to respond to risks while maintaining stability. The goal is to achieve minimal losses during crises, partly by reducing the impact of the human factor (trader errors due to fear and greed).
Risk Optimization
The balance of risk and return in a portfolio is a fundamental principle of portfolio theory. An investor can relatively easily manage portfolio optimization when it includes up to ten securities. However, calculations become more complex as the number of securities increases, raising the likelihood of subjective errors, leading to unexpected losses instead of anticipated profits.
While it’s possible to optimize a portfolio without AI using numerous good software tools, these tools are not capable of predictive analytics in finance. They cannot predict a trend reversal in real time and replace profitable investment instruments with defensive ones. AI excels at this task, reducing risks during market corrections or crises. Moreover, by using AI, an investor can remain profitable when others suffer significant losses and are forced to add extra cash to their accounts.
AI algorithms also work well in a calm market. They analyze market trends, economic indicators, and company reports. As previously mentioned, such models can identify non-obvious patterns that even the most experienced trader might miss. Timely changes in portfolio structure can help capitalize on market inefficiencies, generating additional profit.
Neural networks are indispensable in stressful situations. These intelligent systems also model market scenarios, testing how different portfolio structures perform under severe crisis conditions. Their use helps investors mitigate most risks in extreme situations.
AI and the Future of Investment Risk Management
Today, the application of artificial intelligence in investment risk management systems is very limited. Only large investors, such as hedge funds, asset managers, brokerage firms, and banks, can afford such hardware and software products. This gives them a significant market advantage, allowing them to attract funds from smaller corporate and private players who do not have access to similar tools.
In the future, this picture is likely to change, and AI products will become accessible to anyone interested. Even today, enthusiasts are conducting initial experiments using publicly available generative AI systems like ChatGPT. Although these systems have not undergone machine learning for risk mitigation on financial or market data, the results already suggest that such systems hold a very promising role in future risk management.
This future is primarily tied to the advancement of AI technologies. Experts anticipate that the implementation of new hardware solutions (actively developed by chip manufacturer Nvidia) and software algorithms for learning and decision-making will lead to more complex and sophisticated systems. Innovative models will be capable of more accurately analyzing input data and assessing risks at the output.
AI in Investment Strategies
Investors primarily hope that artificial intelligence will enhance the ability to accurately forecast market developments. Many see this option as a tool for identifying market inefficiencies and gaining competitive advantages, ultimately leading to higher profits.
However, experienced market participants view AI as an excellent tool for risk reduction. Forecasting crisis situations and even market pullbacks will allow timely assessment of potential risk levels and informed decision-making to mitigate them (e.g., through hedging or portfolio restructuring). According to some estimates, the achieved effect could surpass even the benefits of more accurate predictions for optimal trade entry points.
Integration with Real-Time Trading Terminals
Currently, only a few AI systems process market data in real-time. Most systems can only analyze and provide forecasts in an integrated manner, typically medium- to long-term.
Advancements in artificial intelligence models aim to significantly enhance data processing performance (e.g., by incorporating quantum computing modules into systems). This will enable AI risk management tools to perform calculations almost in real-time, thereby increasing the speed of response to any risks.
AI in Portfolio Management
Access to advanced artificial intelligence models is likely to be integrated directly into investor products. This will allow market participants to more accurately identify potential risk levels during the portfolio formation stage.
However, a more effective approach would be to create systems based on AI that not only forecast but also alert users to potential risk increases, assess their potential levels, and provide recommendations for mitigation.
Naturally, AI in risk management will not be a panacea and cannot protect all investors from making incorrect decisions and the resulting losses. However, it is expected that creating investment strategies will become significantly easier. Consequently, almost every market participant will be able to utilize automated and fully automatic risk management systems.
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