-
8 minutes
Why Governance is the Precondition for Scalable AI Agents
Read More ->: Why Governance is the Precondition for Scalable AI AgentsScalable AI agents are quickly moving from experimental tools to embedded components of enterprise infrastructure. In financial services, manufacturing, retail, and other regulated sectors, autonomous systems are beginning to interface directly with ledgers, operational databases, and reporting pipelines. As these systems evolve from conversational assistants into operational actors capable of invoking tools, modifying records, and influencing downstream decisions, their risk profile changes materially. As explored in our article on AI agents in data analytics, these systems can automate everything from data ingestion to predictive insights. Why Traceability Becomes a Governance Requirement At this stage, AI agent performance alone is no…
-
4 minutes
Automating a Risk Control Dashboard with Power BI MCP in Cursor for Free
Read More ->: Automating a Risk Control Dashboard with Power BI MCP in Cursor for FreeModern risk and control dashboards rarely fail because of visuals. They fail upstream, where definitions drift, calculations get re-implemented, and data governance lives in spreadsheets or people’s heads. In this walkthrough, I demonstrate how Power BI’s MCP (Model Context Protocol) can be used inside Cursor to automate much of that foundational work. MCP (Model Context […]
-
4 minutes
How To Use Bayesian versus Frequentist Inference
Read More ->: How To Use Bayesian versus Frequentist InferenceIn financial risk management, debates about Bayesian versus frequentist inference are often framed as methodological or philosophical. In practice, the choice is far more pragmatic: it is primarily a data problem. Model risk, drift, and operational risk live upstream of market, credit, and liquidity models. They are shaped less by elegant theory and more by the realities of data volume, stability, and interpretability. This is where the distinction between frequentist and Bayesian inference becomes operationally meaningful.
CATEGORY ARCHIVES


