AI-Based Investment Risk Assessment System

An investment portfolio of $100 million suffered losses 15% beyond the VaR forecast during a correction—classical historical simulation failed because it didn't account for the sudden jump in correlations. Our team encountered this in a project for a hedge fund and switched to ML-enhanced VaR, which cut the error by 2.5 times compared to traditional Monte Carlo. This situation is typical: standard methods break down in non-stationary conditions when asset correlations shift abruptly. We develop systems for assessing market, credit, and operational risks using ML models that adapt to non-stationarity in correlations—critical for portfolios above $500 million. With over 10 years of experience, we have delivered more than 30 projects for banks and investment funds, including integrations with Bloomberg and Reuters.

How ML Improves VaR

Traditional VaR methods have limitations:

• Historical simulation assumes stationarity, but correlations break during crises.
• Monte Carlo is flexible but slow and requires accurate process models.

ML-enhanced VaR uses neural networks to directly predict distribution tails. For example, QR-LSTM predicts return quantiles while capturing nonlinear dependencies. Comparison of methods:

Accuracy values: historical simulation errs by 30% in the tails, Monte Carlo by 20%, ML-enhanced VaR by 8%. This is confirmed by tests on historical crisis data (see Value at Risk).

Stress Testing: How ML Finds Relevant Scenarios

Scenario analysis: What happens to a portfolio if the S&P drops 50%, VIX hits 80, and credit spreads widen 600bp? The ML component clusters historical periods and selects those similar to current conditions, making tests more relevant. For example, during a market downturn resembling the COVID crisis, the system used scenarios from the global financial crisis adjusted for sector specifics, reducing the deviation from actual outcomes to 5%.

Why Correlations Are Non-Stationary and How DCC-GARCH with ML Solves This

Correlations are non-stationary—during crises everything tends toward 1. We use DCC-GARCH for dynamic correlations plus ML for nonlinear dependencies. Copula models capture tail dependencies, which is critical for hedge funds. DCC-GARCH improves correlation estimates by 30% compared to rolling windows.

Liquidity Risk

Liquidity-adjusted VaR accounts not only for market revaluation but also the cost of rapid liquidation. ML on bid-ask spreads and market depth predicts liquidation costs for large positions—accuracy of 90% vs. 70% for linear models.

Credit Risk in Investments

Credit spread prediction: ML forecasts changes in corporate bond credit spreads using features: rating, financial metrics, macro indicators, industry factors, news sentiment. Prediction error is 25% lower than regression.

Default Probability for private companies: When public data is unavailable, we use bank metrics plus an ML version of the Merton model. Default classification accuracy reaches 85%.

CVA (Credit Valuation Adjustment): For derivatives—integral of PD × EE over time. ML speeds up Expected Exposure simulation by 10x and improves PD accuracy by 15%.

Portfolio Optimization with Risks

Black-Litterman + ML: Bayesian update of market equilibrium using ML views as posterior inputs—model predictions replace subjective expert estimates.

Risk Parity: Equalizing risk contributions. The ML component predicts forward-looking volatility and correlations, leading to a more stable allocation than historical. Portfolio volatility decreases by 20%.

Robust Optimization: Classical mean-variance is sensitive to estimation error. ML provides estimates with lower error; robust approaches (worst-case, uncertainty sets) reduce extreme loss risk to 2% instead of 8%.

Comparison of Input Data for Models

How We Implement ML Risk Systems: Step-by-Step Process

1. Data collection and cleaning: prices, macro, news, liquidity. Volume from 10⁶ records.
2. Architecture selection: LSTM, Transformer, GARCH—tailored to task.
3. Training with validation: backtesting on 80% of data, stress testing on 20%.
4. Integration with portfolio system via REST API.
5. Monitoring and retraining: automatic retraining upon metric drift.

Real-time Risk Dashboard

from fastapi import FastAPI import numpy as np import pandas as pd app = FastAPI() @app.get("/api/portfolio/risk") async def get_portfolio_risk(portfolio_id: str): positions = get_positions(portfolio_id) returns = get_historical_returns(positions) # VaR calculations var_95 = np.percentile(portfolio_returns, 5) var_99 = np.percentile(portfolio_returns, 1) cvar_95 = portfolio_returns[portfolio_returns <= var_95].mean() # Stress test results stress_scenarios = run_stress_tests(positions) # Factor exposures factor_betas = calculate_factor_exposures(positions) return { "var_95_1day": float(var_95), "var_99_1day": float(var_99), "expected_shortfall": float(cvar_95), "max_drawdown_1y": float(calculate_max_drawdown(returns, 252)), "stress_scenarios": stress_scenarios, "factor_exposures": factor_betas, "concentration_risk": calculate_herfindahl_index(positions) } 

Regulatory requirements: Basel III (banks), UCITS/AIFMD (funds), Russian Central Bank requirements for brokers. ML models are documented and validated similarly to SR 11-7.

What the Work Includes

• Development and training of ML models (VaR, stress, credit, liquidity)
• Integration with existing portfolio systems (Bloomberg, Reuters)
• Real-time dashboard on FastAPI with REST API
• Model card and documentation for regulators
• Training of risk management team on system usage
• Support and model retraining for 6 months

Development timeline: from 6 to 12 months for a comprehensive risk engine with regulatory compliance. Pricing is determined individually. Get a consultation—we'll discuss your portfolio and risks. Contact us for an assessment of your project—we guarantee alignment with best practices in financial engineering.