Machine Learning Models That Support Risk Assessment

I really like this paper from researchers at HEC Montréal, titled Deep Implied Volatility Factor Models for Stock Options.  It elegantly solves the classic trade-off between modern machine learning and traditional financial modeling. Sam Cohen, Lukasz Szpruch, and myself have written about the benefit of these hybrid models in "Black-box model risk in finance". The computationally "heavy" part—training the neural network to learn the complex volatility shapes—is done only once. Pure machine learning models are often "black boxes," which regulators and risk managers dislike. It blends power with interpretability. 1. One-Time Training: A neural network is trained once on historical data to learn a stock's unique basis factors for volatility, including its specific pre-earnings ramp-up shape. 2. Daily Data Ingestion: Each day, ingest current market data for all traded options: their moneyness, maturity, implied volatility, and the time-to-earnings-announcement (TTEA). 3. Rapid Daily Fitting: Perform a fast daily linear regression (OLS) to calculate the factor loadings (betas) that best fit the day's observed market prices using the pre-trained basis factors. 4. Construct IV Surface: The daily betas yield a complete, smooth function for the entire IV surface, allowing for immediate and consistent pricing of any option, including non-standard strikes. 5. Derive Risk Metrics: Use the complete surface to compute advanced metrics like the stock's risk-neutral probability distribution or a custom, 30-day VIX-style index for targeted risk analysis.

Introducing Generative AI for Corporate Risk Assessment. How? By using words, lots of words, served up in the transcripts of earnings calls! Let's break it down: A recent working paper from the University of Chicago explores an emerging application of generative AI: using large language models to uncover dimensions of corporate risk by analyzing earnings call transcripts. Generative AI tools like GPT-3 have demonstrated an ability to understand nuanced language through a massive store of world knowledge. This introduces potential to distill useful insights from unstructured disclosure texts in a way that traditional methods cannot. The study, "From Transcripts to Insights: Uncovering Corporate Risks Using Generative AI" (Kim et al., 2023) leverages GPT-3.5 to construct new measures of political, climate, and AI-related risk exposure at the firm level. Here's a direct link: https://lnkd.in/gbB_6VUu It finds these AI-derived measures exhibit plausible behavior and possess information relevant to predicting future stock volatility and firms' policies. While preliminary, these results indicate generative language models warrant further exploration as a risk assessment tool. High-Level Takeaways • Motivation: Traditional methods relying on topic dictionaries have limitations, whereas generative AI can understand deeper context and general knowledge. • Methodology: GPT-3.5 was used to summarize and assess risks mentioned in earnings calls, converting outputs to quantitative exposures. • Preliminary Results: GPT measures displayed industry and time variation, and associations with market outcomes. Most variation was firm-specific. • Limitations: Single model; word counts to quantify risk; potential for external influence on GPT; endogeneity concerns remain. The study provides early glimpses into generative AI's utility for interpreting unstructured disclosure texts on complex topics like corporate risks. Future work addressing limitations could draw stronger conclusions about AI's viability in this domain versus other approaches. While just a starting point, these findings offer tentative encouragement around this use-case for generative models' practical application. In summary, while it's definitely just preliminary, I think this study tends to confirm my own (admittedly more ad Hoc) experimentation with using generative AI to analyze transcripts of conversations and report or announcement type speech, namely, it's actually pretty good at this! Moreover, the results I've got using GPT-4 and Claude 2 100k have frequently been significantly better than the results with GPT-3.5, which is what the researchers used for this study. So, I think this is a good use case and worth pursuing. 

Mastering the Architecture of Risk: A Quant’s Blueprint for Modern Financial Stability The Risk Management Framework: A Closer Look A firm’s risk management structure consists of five key areas, each integrating quant models for predictive insights: → Operational Risk: Focuses on internal processes, with roles like Capital & Risk Managers, Data & Metrics, and Modeling. → Credit Risk: Handles default risk and counterparty exposure, utilizing ML models for predictive analytics. → Market Risk: Uses VaR, stochastic volatility, and PCA for factor analysis and hedging market movements. → Liquidity & Treasury Risk: Ensures liquidity with Cashflow-at-Risk models and real-time funding strategies. → Infrastructure & Analytics: Supports quant-driven decision-making through model validation, data pipelines, and AI-driven insights. How Quants Drive Risk Management Quants are at the core of modern risk management, using stochastic models, AI, and reinforcement learning to optimize decisions. → Market Risk: ✔ BlackRock’s reinforcement learning models simulated tail events 10x faster, reducing portfolio drawdowns by 14% during the 2025 Liquidity Squeeze. → Credit Risk: ✔ Morgan Stanley’s ML-driven Probability of Default (PD) model flagged high-risk sectors six months early, saving $1.2B in corporate loan losses. → Liquidity Risk: ✔ Goldman Sachs’ Liquidity Buffers 2.0 dynamically adjusted reserves in real-time, cutting funding gaps by 22% in the 2024 repo crisis. These advances show how quants translate data into actionable risk insights, meeting Basel IV’s new explainable AI mandates. Emerging Trends: Where Risk Meets AI & Quantum As financial complexity increases, firms are integrating AI, reinforcement learning, and quantum optimization into risk models: → AI & Generative Modeling: ✔ Bloomberg’s “SynthRisk” generates 10M+ synthetic crisis scenarios to train resilient risk models. ✔ Citadel’s RL-driven treasury system autonomously hedges FX exposure, saving $220M annually in slippage. → Regulatory Arbitrage & Basel IV: ✔ EU banks use quantum annealing to optimize Risk-Weighted Assets (RWA), freeing up $15B in trapped capital. → Ethical AI & Bias-Free Risk Models: ✔ The 2026 SEC mandate requires federated learning to prevent bias in credit scoring and risk assessments. The Bottom Line Risk management is no longer just about avoiding disasters—it’s about engineering resilience while optimizing for alpha. For quants, this means: → Translating Basel IV constraints into convex optimization problems. → Turning unstructured data (news, tweets, satellite imagery) into real-time risk signals. → Balancing AI’s predictive power with explainability for compliance and interpretability. How are you reinventing risk frameworks in the AI era? Let’s discuss. #RiskManagement #QuantFinance #FinancialEngineering #MarketRisk #AIinFinance #BaselIV #LiquidityRisk #HedgeFunds #TradingStrategies #MachineLearning #AlgorithmicTrading

Financial organizations struggle to predict the credit risk of millions of members. That’s because everyone has unique spending habits, plus economic conditions vary. This Azure-powered risk management architecture addresses these challenges and evaluates default probabilities: 1️⃣ Data Integration Collect and unify transaction histories and credit scores using Azure Data Lake Storage, processed via Data Factory, and analyzed with Synapse Analytics. 2️⃣ Data Preprocessing Clean, enrich, and prepare data with Azure Synapse and Data Factory for seamless analysis. 3️⃣ AI-Driven Model Development Build, train, and evaluate credit risk models in Azure Machine Learning, integrating feature engineering, fairness checks, and interpretability. 4️⃣ Flexible Deployment Deploy models on Managed Endpoints for both real-time and batch inference. 5️⃣ Real-Time and Batch Predictions Enable fast and accurate predictions through APIs or data pipelines, catering to diverse use cases. 6️⃣ Actionable Insights Visualize predictions and trends in Power BI, empowering smarter and transparent loan decisions. 7️⃣ End-to-End Reporting Generate detailed performance metrics with tools like Synapse Analytics and SQL for ongoing monitoring. The result? ⚡ Scalable credit risk assessments ⚡ More accurate default predictions ⚡ Fair and responsible loan decisions How do you see AI simplifying credit risk modeling? Let’s discuss! #Azure #AI #CreditRisk #FinTech #MachineLearning #DataAnalytics