The team at a16z, which includes Angela Strange, Anish Acharya, Sumeet Singh, Alex Rampell, Marc Andrusko, Joe Schmidt, David Haber, and Seema Amble, recently published their post Financial Services Will Embrace Generative AI Faster Than You Think. In this post, they outline what could be “the largest transformation the financial services market …
Introduction While Model Risk Management (MRM) principles are important, meeting the expectations set by regulators (FED 2011, PRA 2023) and fostering a robust risk management framework can be challenging or even confusing at times. We believe all MRM practitioners must develop a good understanding of …
The Prudential Regulation Authority’s (PRA) supervisory statement SS1/13 Model Risk Principles for All Banks, signifies a major shift in model risk management (MRM) for banks, and emphasizes the need for clear accountability, rigorous governance, and continuous monitoring and validation of models. The role of technological …
Throughout history, many human inventions have been democratized as technology has advanced, from the wheel to the printing press, to the smartphone. More recently, advances in the areas of computing power, programming capabilities, and data/information availability have resulted in a proliferation in the development and …
Uncertainty plays a critical role in AI model risk management since it can affect their performance, reliability, and ability to make accurate predictions. Practitioners involved with these models, depending on their role within organizations, may not have a complete understanding of the concepts behind model …
Models deploy into more and more areas of business operations in Finance, Healthcare, Retail, and many other industries bringing benefits to these institutions but also exposing them to greater operational, reputational, and regulatory risks. This is especially true in the Financial Services and Insurance industries, …