AIFMD II's liquidity management tool rules are now in force, with the core selection requirement applying to every open-ended fund — new and existing — from April 16, 2026. Here's what AIFMD actually requires for liquidity stress testing, what's changed, and why your LST methodology now feeds directly into a binding compliance decision.

SR 11-7 governed model risk for fifteen years. SR 26-2 replaces it with six concrete changes — a narrower model definition, risk-based validation cadence, more flexible validator independence, a shift to non-binding guidance, a scope weighted toward larger institutions, and an explicit AI carve-out. Here's what each change means in practice for your MRM program.

Backtesting answers one question well: did this work before? It was never designed to answer the question that matters just as much — would it survive something new? Here's where backtesting structurally falls short, and what forward-looking risk teams add alongside it.

On April 17, 2026, the Fed, FDIC and OCC replaced SR 11-7 with SR 26-2 — a new model risk management framework fifteen years in the making. Here's what changed, what it means for your institution, and the risk gap no regulatory framework can close.

Time-series foundation models like TimeGPT and TimesFM deliver powerful zero-shot forecasting, but in finance—with heavy tails, volatility clustering, and regime shifts—they excel after fine-tuning. Key to reliable use: strong evaluation, contamination checks, and synthetic data for better tail modeling.

Most forecasting models perform flawlessly on historical data — until the world changes. In this first article of our four-part series, we explore why relying on history alone leaves models exposed, and how synthetic data can help stress-test forecasting systems beyond their comfort zones. By simulating volatility spikes, regime shifts, and tail events, synthetic data reveals blind spots that real data hides — helping models move from mere accuracy toward true resilience.