AI Model Explainability in Quant Finance: Experts Debate

A quant fund manager + A HFT prop desk founder + A quant teacher = a session worth watching On 9 April, we hosted Kelvin Foo, Dr Gaurav Raizada, and Vivek Krishnamoorthy for a workshop on Algorithmic Trading & Options Risk Management. Watch the recording: www.quantinsti.com/articles/algorithmic-trading-python-ai-options-risk-management-webinar/ . . Learn to apply AI and ML in trading in a practical hands-on manner EPAT syllabus on Machine learning & AI: https://bit.ly/4n1VmPJ Free self-paced course for beginners: https://bit.ly/46UMjeF Apply AI in trading strategies: https://bit.ly/4mZHaGG AI in portfolio management: https://bit.ly/4ocH0gg ----- In this expert panel discussion, industry leaders unpack the critical importance of AI model explainability, robustness, and governance in quantitative finance. Learn how to bound model risk, implement best practices for explainability, and prepare for evolving regulatory requirements. 🎯 What You’ll Learn: - Why explainability is core to quant finance—not just gambling - Techniques to bound model risk using epsilon-based comparisons - Best practices for LLM-generated documentation, testing, and compliance - How to manage data governance and prevent model hallucinations - The role of AI governance policies and upcoming regulatory changes ⏰ Timestamps: 00:00 Preview – The debate over explainability 00:45 Prodipta Ghosh (QuantInsti) asks about best practices 01:08 Peter Cotton (Global Strategic Minerals) on bounding model risk 02:59 Matteo Campellone (Brain) on feature-importance and data quality 04:17 Dimitri Bianco (Fancy Quant) on accountability and risk committees 05:14 Faisal Mohammed (Zerodha) on regulatory outlook and AI governance 🎓 Featured Experts: Prodipta Ghosh – Vice President, QuantInsti Peter Cotton – CTO, Global Strategic Minerals Corporation Matteo Campellone – Chairman & Head of Research, Brain Dimitri Bianco – Founder, Fancy Quant Faisal Mohammed – Vice President, Trading Operations, Zerodha Perfect For: Quantitative traders, AI researchers in finance, compliance officers, and anyone building or governing AI-driven trading systems. #AIExplainability #QuantFinance #ModelGovernance #AlgorithmicTrading #AICompliance #RiskManagement #Fintech #MachineLearning #TradingRegulations Keywords AI explainability, quant finance, model robustness, algorithmic trading, AI governance, model risk, financial compliance, LLM documentation, trading operations