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Method for Cross-functional risk-adjusted planning

IP.com Disclosure Number: IPCOM000213564D
Publication Date: 2011-Dec-21
Document File: 2 page(s) / 23K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a method to integrate operational risk management into the planning and budgeting process. The method implements risk adjusted planning by integrating rigorous quantitative analytics with budgeting and enabling the continuous assessment of risk exposure through feedback systems.

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Method for Cross-functional risk-adjusted planning

As a result of the recent financial crises, the subject of operational risk management has gained attention and popularity over the past few years. The inability of financial institutions to gauge risk properly can have dire consequences, and this has led to a variety of regulatory reforms across the globe.

Current operational risk management software provides singular elements of the operational risk management process and is unsuitable for end-to-end use. As a result, financial institutions utilize decentralized risk management systems across multiple products, making optimal risk management difficult.

Current products are fragmented and do not centralize all components of risk management. This creates an opportunity for software for end-to-end operational risk management solution. Current risk management software integrates risk assessment and analytics into the business processes of planning, budgeting, forecasting, and reporting. To achieve this, the software facilitates the execution of novel business processes that are not available in any other risk management product. By centralizing the management of operational risk and introducing key risk indicators, controls, and other risk metrics into the planning process, institutions can communicate more expediently and react more intelligently to operational risk.

Current solutions do not integrate operational risk management into the planning and budgeting process. The solution described here implements risk adjusted planning by integrating rigorous quantitative analytics with budgeting and enabling the continuous assessment of risk exposure through feedback systems.

Using a database of loss events, quantitative algorithms discern risk metrics such as severity distributions, frequency distributions, correlations between risk categories and key risk indica...