Swan.my.id | Jakarta, Indonesia - An AI Portfolio Risk Check can help investors understand how much risk exists in a portfolio before losses become difficult to recover. Artificial intelligence can process portfolio data, calculate exposure, and test different market scenarios within seconds.
The use of AI in investing is also becoming more common. A survey of 938 U.S. retail investors by Investing.com, released on April 6, 2026, found that 62% already use AI to support investment decisions. However, around 39% said they were concerned about incorrect or misleading AI recommendations.
Because of that, AI should be treated as an analytical tool rather than a replacement for investor judgment. It can help measure concentration, volatility, position size, and potential drawdowns. However, investors still need to verify the data and decide how much risk they are willing to accept.
What Is an AI Portfolio Risk Check?
An AI Portfolio Risk Check is a process that uses artificial intelligence to identify, measure, and monitor risks across an investment portfolio.
The process can cover several areas. These include asset concentration, price volatility, position size, drawdowns, and correlations between assets.
The main benefit is speed. AI can process large amounts of information faster than manual calculations. However, the result depends on the quality and completeness of the data provided.
AI also does not automatically understand an investor's financial goals, emergency fund needs, investment horizon, or personal risk tolerance. Therefore, its output should be used as supporting information.
Key points to remember
- AI can calculate portfolio exposure and position size.
- AI can help identify concentration and volatility risks.
- AI can simulate different market declines.
- AI cannot reliably predict unexpected market events.
- AI-generated results should be checked against reliable market data.
- The final risk limits remain the investor's responsibility.
Why Portfolio Losses Can Be Difficult to Recover
One important part of risk management is understanding the mathematics of losses.
A portfolio that falls 50% does not need another 50% gain to recover. It needs to double from its reduced value. Therefore, a 50% loss requires a 100% gain to return to the original capital.
This creates an asymmetric relationship between losses and recovery.
A simulation presented by Pluang on September 24, 2026, illustrates the relationship:
| Portfolio Loss | Gain Needed to Recover |
|---|---|
| 10% | 11.1% |
| 20% | 25.0% |
| 30% | 42.9% |
| 50% | 100.0% |
| 70% | 233.3% |
The formula is:
Required gain = 1 ÷ (1 − loss) − 1
For example, after a 30% decline, the portfolio has only 70% of its original value. It therefore needs a gain of about 42.9% to return to the starting level.
This is only a mathematical simulation. Actual investment results can differ because market prices, fees, liquidity, and execution conditions can affect performance.
5 Steps for an AI Portfolio Risk Check
Investors can use a general AI assistant or an investment platform with AI features to review portfolio risks. However, the investor must provide accurate portfolio information.
1. Map Every Asset and Its Allocation
Start by listing every asset and its current value.
The goal is to understand how much of the portfolio is exposed to each asset, sector, or currency.
For example, an investor could ask an AI tool to calculate the percentage allocation of each asset and highlight positions above a specific threshold.
A simple prompt could be:
“Here is my portfolio and the current value of each asset. Calculate the percentage allocation and identify assets that represent more than 25% of the portfolio.”
A large allocation to one asset can make the portfolio more sensitive to a single event.
However, diversification does not eliminate risk. Assets from different categories can still move in the same direction during periods of market stress.
2. Calculate Position Size and Transaction Risk
Position size determines how much capital is exposed to a potential loss.
A commonly used calculation is:
Position size = (Capital × Risk Percentage) ÷ (Entry Price − Stop-Loss Price)
Consider a hypothetical example.
An investor has Rp100 million in capital and uses a 1% risk assumption. That equals Rp1 million of potential risk.
If the entry price is Rp10,000 and the stop-loss level is Rp9,000, the difference is Rp1,000 per unit.
The maximum position under this simulation would therefore be 1,000 units. At Rp10,000 per unit, the position would be worth Rp10 million.
This example is only for educational purposes. The 1% figure is not a universal recommendation. Prices can also move beyond a stop-loss level, resulting in a larger actual loss.
3. Check Volatility and Correlation
Volatility measures how sharply an asset's price moves over time.
An AI tool can help calculate average daily movements, historical drawdowns, and correlations between assets.
Correlation is particularly important for diversification.
Two assets may appear different, but they can still have a high correlation. If both decline at the same time, the portfolio may receive less diversification benefit than expected.
Therefore, investors should look beyond the number of assets they own. The relationship between those assets also matters.
4. Run Downside Scenarios
The fourth step is to test hypothetical market declines.
For example, investors can ask an AI tool to calculate what happens if every asset falls by 10%, 20%, or 30%.
They can also test different assumptions for individual assets.
The purpose is not to predict what the market will do. Instead, scenario analysis shows how much damage different market conditions could cause.
This can help investors compare potential losses with their personal risk limits.
5. Verify the AI Results
The final step is verification.
AI calculations can contain errors, especially when the input data is incomplete, outdated, or incorrectly entered.
A useful verification process includes:
- Checking prices against current market data.
- Recalculating at least one important figure manually.
- Reviewing portfolio allocations after major transactions.
- Comparing AI output with information from reliable sources.
- Updating the analysis when asset weights change significantly.
Investing.com's April 2026 survey found that about 54% of respondents only partially trusted AI results and verified them through other sources.
That behavior highlights an important principle: AI can accelerate analysis, but verification remains essential.
How Pluang Uses AI for Investment Analysis
Pluang offers Aura AI for U.S. stocks and crypto assets.
According to the feature information accessed on September 24, 2026, users can open an asset's detail page and tap the AI button. The feature provides analysis covering technical factors, fundamentals, sentiment, risks, and potential catalysts.
For U.S. stocks, the analysis is updated every three hours while the market is open and every six hours while it is closed. Crypto analysis is updated every six hours.
Aura AI does not provide buy or sell recommendations. Its feature information also does not indicate that it calculates risk across an investor's entire portfolio automatically.
For broader portfolio analysis, Pluang introduced Agentic Trading on August 3, 2026. The feature connects investment accounts with AI systems such as ChatGPT, Claude, or Gemini for portfolio analysis.
At launch, the feature was available as a limited beta for selected users. Transactions required manual confirmation under a propose-confirm-execute process. Its current availability may change, so users should check the latest information inside the application.
Meanwhile, Screeners can help investors filter assets based on selected indicators. Web Trading also provides portfolio and order views, along with multi-chart functionality.
The Limits of AI in Portfolio Risk Analysis
AI does not eliminate investment risk.
The quality of its analysis depends heavily on the data it receives. Market information can also be delayed, incomplete, or temporarily inaccurate.
Furthermore, AI cannot know every factor affecting an investor's financial situation.
For example, an AI model may not know whether an investor needs the money for an emergency, plans to use it within six months, or has other financial obligations.
Unexpected events also create another limitation. Markets can react quickly to geopolitical developments, economic announcements, corporate news, or sudden liquidity changes.
As a result, AI analysis should not be treated as a forecast of future prices.
Investors should also avoid sharing sensitive account information with AI tools. Passwords, PINs, authentication codes, and OTPs should never be provided to an AI assistant.
Common Mistakes When Using AI for Risk Management
Several mistakes can reduce the usefulness of AI-based portfolio analysis.
The first is treating AI output as a direct trading recommendation. AI can provide calculations and analysis, but it cannot determine whether a particular decision fits an individual's financial circumstances.
The second is providing incomplete information. Missing positions, incorrect prices, or outdated portfolio values can produce misleading results.
The third is setting risk limits only after a position starts losing money. Risk parameters are generally more useful when established before a transaction.
The fourth is underestimating the risks of volatile products. Crypto and U.S. stocks can experience significant price movements, even when investors have conducted detailed analysis.
Finally, investors should avoid assuming that two AI tools will produce identical results. Different models can use different datasets, assumptions, update schedules, and calculation methods.
Can AI Replace Manual Risk Analysis?
AI can make risk analysis faster, but it does not completely replace manual review.
An effective workflow can combine both approaches. Investors can use AI to calculate exposure, test scenarios, and identify potential concentration risks.
They can then verify important figures manually and compare them with current market information.
The five-step process is straightforward:
- Map portfolio allocations.
- Calculate position size.
- Review volatility and correlation.
- Test downside scenarios.
- Verify the results regularly.
This approach can make portfolio monitoring more structured without treating AI as a source of certainty.
Final Takeaway
An AI Portfolio Risk Check can help investors understand the risks inside their portfolios before making financial decisions. It can identify concentration, estimate position sizes, measure volatility, examine correlations, and simulate potential declines.
However, AI has clear limitations. Data can be incomplete, calculations can contain errors, and unexpected market events cannot be predicted reliably.
Therefore, the role of AI is best understood as an analytical assistant. Investors still need to establish their own risk limits, verify important figures, and consider their financial circumstances.
For anyone using AI to review investments, the basic principle remains simple: use AI to improve the analysis, but keep the final decision under human control.
Disclaimer
This article is for informational and educational purposes only. It is not an invitation or recommendation to buy or sell any particular investment product.
Investing involves risks and the possibility of losing capital. Past performance does not guarantee future results. Investors should conduct their own analysis and consider their financial circumstances, investment objectives, and risk tolerance before making any investment decision.
AI-generated analysis may contain errors, delays, or incomplete information. It should not be treated as financial advice or a guarantee of future market performance.
U.S. stocks, ETFs, crypto assets, and other investment products may carry significant risks. Investors should understand the relevant product terms and risks before using them.