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.

Industry AI Solutions: Healthcare, Finance, Retail, Manufacturing

We encounter the same pain points: a general text model doesn’t distinguish medical nomenclature, and a standard object detector confuses “weld seam scratch” with “casing scratch.” Each time these are different defects with different consequences. To avoid this, we build industry-specific solutions on top of general methods, but with deep domain knowledge — from regulatory requirements to data specifics. Over 5 years, we have completed 80+ projects in fintech, healthcare, retail, and manufacturing, and none were without adaptation to a specific business case.

Healthcare: Regulatory Maze and Data Governance

Medical AI differs not in technical algorithms but in a compliance-first approach. Depending on the country of application, the model may be a Class II or III medical device requiring clinical trials (FDA, CE MDR, GOST R). We ensure compliance with these standards at the architecture stage — fixing them post-factum is 10× more expensive.

Medical imaging. Detection on X‑rays, CT, MRI is a mature area. Models on ResNet, EfficientNet, SegFormer achieve AUC 0.94–0.97 on standard tasks (pneumonia on CXR, polyps on colonoscopy). Key issue is generalization: a model trained on data from one scanner manufacturer degrades on another due to differences in preprocessing and artifacts. Solution: domain adaptation via MONAI (Medical Open Network for AI) from NVIDIA, which includes DICOM loading, 3D augmentation, and confidence calibration. TotalSegmentator — for automatic segmentation of 117 structures on CT, production‑ready, Apache 2.0 license.

Clinical NLP. Extracting structured information from clinical records: diagnoses (ICD‑10/11), prescriptions, dates, indicators. medspaCy, scispaCy, MedCAT — specialized NLP libraries with ontologies (SNOMED‑CT, UMLS). Fine‑tuning BioBERT or ClinicalBERT on our data yields F1 0.85–0.92 on NER tasks versus F1 0.65–0.72 for general BERT. We verified this on a project with a regional oncology center — cancer stage extraction accuracy increased by 23%.

Clinical decision support. LLM assistants for clinical decision support are a regulatory gray area. We use an RAG system on top of clinical guidelines (UpToDate, local protocols) with explicit citation for each statement. The model does not diagnose but helps find relevant protocols. Stack: LlamaIndex + pgvector + pubmedbert-base-embeddings + Llama Guard for safety. Data in DICOM/HL7 FHIR, on‑premise deployment mandatory.

Finance: How to Ensure Interpretability of a Scoring Model under Basel IV?

The financial sector is one of the most mature in applying ML, but regulation is maximal. Every model affecting credit decisions falls under Basel IV, EU AI Act, GDPR Article 22. We deliver AI solutions for fintech that satisfy these requirements — in a project for a top‑10 bank we deployed a scoring model where each record required SHAP explanations.

Credit scoring. Gradient boosting (LightGBM, XGBoost) dominates. Neural networks yield +0.5–2% AUC but lose interpretability. Standard: LightGBM + SHAP to explain each decision. Fairness checking is mandatory: Fairlearn or aif360 for auditing disparate impact on protected attributes (age, gender). The default class is 1–5% — with an imbalance of 1:30, a model with 97% accuracy may have recall 0.2. Solution: focal loss, class_weight='balanced', SMOTE + careful validation. In one fintech scoring project, the model reduced credit losses by $2.1 million annually.

Algorithmic trading and risk management. LSTM and Transformer for price forecasting are popular but unstable in production due to non‑stationarity of financial series. A more robust approach: ML for signal generation (classification: up/down over horizon N) with traditional portfolio optimization on top. Backtesting via Zipline‑Reloaded, vectorbt, QuantLib. Proper backtesting is critical — look‑ahead bias kills results. We guarantee a clean experiment: all data at signal time is available in real time.

AML (Anti‑Money Laundering). Graph Neural Networks for analyzing transaction networks is an actively developing area. PyG, DGL for GNN. Task: detect suspicious patterns in transaction graphs (layering, structuring). Recall is more critical than precision — better 10 false alarms than miss one money laundering. In a project for a large payment service, we increased recall by 18% without increasing false positive rate.

Retail and e‑commerce: Recommendation Systems and Demand Forecasting

Recommendation systems. Current architectural standard: two‑tower model for retrieval + ranking with cross‑features. TensorFlow Recommenders or Merlin from NVIDIA for GPU‑accelerated feature processing. For small catalogs (<100k items), LightFM is sufficient. A common mistake is training on implicit feedback without accounting for position bias. Solution: IPW (Inverse Propensity Weighting) or randomized logging on a portion of traffic. Development time for a basic recommendation system is 4–8 weeks, including A/B test.

Demand forecasting and inventory optimization. Hierarchical forecasting: SKU → category → store → region. HierarchicalForecast from Nixtla automatically reconciles forecasts across levels. TFT or N‑HiTS for base forecast, gradient boosting for adjustment on exogenous factors (promotions, weather, events). One retail project led to a 15% reduction in stock‑outs due to precise promotion calibration.

Visual search and size compatibility. CLIP embeddings for image search — deploy in 2–3 weeks: clip‑ViT‑B‑32 or clip‑ViT‑L‑14, Faiss or Qdrant index, REST API. For size recommendation — specific models on return data and reviews with fit indication.

Manufacturing: Quality Inspection and Predictive Maintenance

Quality control and defect detection. CV models for product inspection are one of the most mature industry tasks. YOLOv10 for defect detection, SegFormer for segmentation. Specifics: class imbalance (defects are rare), high recall requirement (missing a defect is worse than false alarm). Typical dataset: 500–2000 defect images + 500–1000 normal. Few‑shot learning via DINO or SAM 2 works with 50–100 annotated examples. We gained experience on an electronics production line — recall 0.95 at FPR 0.03. A predictive maintenance deployment saved a manufacturing client $500,000 per year in unplanned downtime.

Predictive maintenance. Vibration sensors, current sensors, thermocouples → feature extraction → anomaly or mode classification. Models: LSTM‑AE for unsupervised, LightGBM for supervised (if failure history is available). Integration with SCADA/OPC‑UA via opcua-asyncio or MQTT. Key metric: False Negative Rate — a missed pre‑failure is more costly than a false alarm. Threshold tuned to business cost of each error type. Timeline: 3 to 6 months to production.

Digital twin and simulation. Surrogate models — ML models replacing expensive physical simulation. If a CFD simulation takes 6 hours and a surrogate (trained on 10,000 simulations) takes 0.01 seconds, that's 2,000,000× speedup for optimization. SALib for sensitivity analysis, botorch for Bayesian optimization on top of surrogate.

General Principles of Industry AI

Regardless of industry, there are patterns that work everywhere. Data matters more than architecture. In healthcare, 1000 quality labeled images are better than 100,000 poor ones. In manufacturing, 200 real defect examples are more valuable than 10,000 synthetic ones. Compliance‑first design — regulatory requirements are easier to embed into architecture from the start than to add later. Logging, explainability, versioning from day one. Domain expert on the team — an ML engineer without domain knowledge does slowly and error‑prone what an ML engineer plus a doctor/financier/technologist does quickly and correctly.

We guarantee certification to customer requirements (ISO 13485, SOC 2, GDPR) and provide full model documentation (model card, datasheet, compliance report). Our experience: 10,000+ engineering hours and 80+ projects.

Work Process for an Industry AI Solution

1. Domain immersion (2–3 days) — interviews with experts, studying regulatory requirements, auditing available data.
2. MVP design (1–2 weeks) — stack and architecture selection, feasibility assessment.
3. Development and validation (from 4 weeks to 6 months depending on industry) — model training, testing, compliance.
4. Integration and deployment (1–4 weeks) — on‑premise / cloud / edge, documentation, staff training.
5. Support and monitoring — model drift, retraining, SLA.

Estimated timelines:

Cost is calculated individually for each project. Get a consultation — we will evaluate your dataset, regulatory map, and business goals.

Why Choose Our Industry AI Solutions?

• 80+ completed projects in fintech, healthcare, retail, and manufacturing.
• 5 years on the market — proven experience with compliance and deployment.
• Quality guarantee: we ensure target metrics (AUC, recall, latency p99) and provide full documentation.
• Licensed technologies: PyTorch, MONAI, LightGBM, Qdrant — we use open‑source with commercially safe licenses.
• Flexibility: we work as a contractor or as an extension of your team.

Contact us for a free data audit and consultation. Request a proposal with a detailed work plan. We will discuss your task and prepare a commercial proposal.