Cybersecurity. AI Governance. Data Science.
8 years building AI systems that regulators can audit and executives can trust, focused on financial compliance, model risk, and responsible AI.
The Federal Reserve's November 2025 Statement of Supervisory Operating Principles signals a seismic shift — from checkbox compliance to material risk. Here's what changed and why it matters.
AI GovernanceA comprehensive framework for analyzing open-source GenAI across near, mid, and long-term development stages — and why the benefits generally outweigh the risks when governance keeps pace.
Case StudiesAn EDA of 2007–2011 lending data to identify the driving factors behind loan defaults — amount-to-income ratios, revolving utilisation, derogatory records, and loan purpose all tell a story.
Something that comes up a lot in financial services AI: the gap between running an LLM eval and producing something regulators can actually work with. ROUGE scores don't tell you which SR 11-7 clause you're satisfying. B…
𝑪𝒍𝒂𝒖𝒅𝒆 𝒋𝒖𝒔𝒕 𝒑𝒓𝒐𝒗𝒆𝒅 𝒚𝒐𝒖𝒓 𝒎𝒐𝒅𝒆𝒍 𝒗𝒂𝒍𝒊𝒅𝒂𝒕𝒊𝒐𝒏 𝒎𝒆𝒕𝒓𝒊𝒄𝒔 𝒎𝒊𝒈𝒉𝒕 𝒃𝒆 𝒂 𝒍𝒊𝒆. Anthropic’s recent report on "Eval Awareness" is more than just a cool AI story. For Model Risk Manage…
𝑾𝒆 𝒏𝒆𝒆𝒅 𝒕𝒐 𝒔𝒕𝒐𝒑 𝒕𝒓𝒆𝒂𝒕𝒊𝒏𝒈 𝑳𝑳𝑴𝒔 𝒍𝒊𝒌𝒆 𝒕𝒉𝒆𝒚 𝒔𝒑𝒆𝒂𝒌 𝑬𝒏𝒈𝒍𝒊𝒔𝒉. 🤐 Following up on that "Prompt Repetition" paper from Google (arXiv:2512.14982), a few other findings are making it clea…
The Federal Reserve's November 2025 Statement of Supervisory Operating Principles signals a seismic shift — from checkbox compliance to material risk. Here's what changed and why it matters.
Fast.ai uncovered something strange in LLM fine-tuning: training loss dropped suddenly after just one pass through the data — suggesting models can memorize inputs almost immediately. Here's what it means.
A comprehensive framework for analyzing open-source GenAI across near, mid, and long-term development stages — and why the benefits generally outweigh the risks when governance keeps pace.
Re-training LLMs from scratch when new data arrives is prohibitively expensive. Three simple strategies — LR re-warming, LR re-decaying, and minimal data replay — match the performance of full re-training at a fraction of the cost.
Traditional ensemble methods fail when correct answers are in the minority. AoR introduces hierarchical reasoning chain evaluation and dynamic sampling to fix this — and consistently outperforms standard approaches.
