Jack L.King – Operatinal Risk
JACK L. KING is the managing director of Genoa (UK) Limited, a consulting and software company specialising in risk. He brings to the firm almost 30 years’ experience in technology and its application to risk.
From August 1998 to November 2000 Dr King worked as Director, Operational Risk for Algorithmics, Incorporated. Before joining Algorithmics, Dr King was a Director in the New York Financial Consulting Practice of Price Waterhouse, with a concentration in market and credit risk. From 1992 – 1996, Dr King was a scientist with the United Nations’ International Atomic Energy Agency in Vienna, Austria where he developed enhanced systems for the measurement and control of global nuclear risk. Operatinal Risk
Dr King’s education includes a Ph.D. in Computer Science, MBA, MA Finance, BS Computer Science, and BS Electrical Engineering. He is a member of IEEE and ACM and is a frequent contributor to risk-related magazines and newsletters.
Operational risk is emerging as the third leg of an institutional risk strategy for financial institutions. Now recognized as a potential source of financial waste, operational risk has become the subject of surveys, analysis, and the search for a comprehenvise set of definitions and a shared framework. Written by a leading expert on operational risk measurement, this important work puts forth a cradle-to-grave hands-on approach that concentrates on measurement of risk in order to provide the needed feedback for managing and mitigating it. Using both theoretical and practical material, he lays out a foundation theory that can be applied and refined for application in the financial sector and beyond which includes a new technique called Delta-EVT(trademark). This technique is a combination of two existing methods which provides for the complete measurement of operational risk loss. The book contains comprehensive step-by-step descriptions based on real-world examples, formulas and procedures for calculating many common risk measures and building causal models using Bayesian networks, and background for understanding the history and motivation for addressing operational risk.