A risk-based performance pipe dream?

THE PANEL

• Andrew Aziz, director, strategy, methodology and quantitative finance, IBM
• Bertrand Hassani, group head of non-financial and operational risk methodology, Grupo Santander
• Peyman Mestchian, managing partner, Chartis
• Joel Clark, moderator, consulting editor, Risk.net

Making better, smarter, more confident decisions is one of the key goals of enterprise risk management (ERM). In theory, if an organisation understands its exposure fully, as well as the returns it is making – or expects to make – and the costs, capital and other resources it consumes, it will be better equipped to price a product or service, set staff compensation, weigh an acquisition or disposal, and consider geographic expansion.

The problem is that the goal more often resembles a pipe dream. It requires levels of integration between risk and finance that few organisations possess, as well as squeaky clean, standardised data, and effective automation – meaning, in turn, that someone at the top of the firm has to demand it, and then push it through.

For banks, meanwhile, incoming regulation – in the form of standardised regulatory capital calculations and disclosures – is resulting in proliferating metrics, and threatens to undermine existing risk measures. Some risk managers fear risk-insensitive standardised numbers may become the measure on which analysts and investors judge the industry. 

In this IBM-sponsored webcast, a panel of industry experts discusses the obstacles facing risk-based performance management and Peyman Mestchian, managing partner, Chartis, reviews findings of a new study on risk-based performance management (one of several research papers in Chartis' The Risk Enabled Enterprise research programme). A number of driving factors are examined, including:

• The future of risk modelling and the fragmentation of metrics
• How to bridge the divide between risk and finance
• The challenge of data aggregation and reporting
• Important technology innovations in enterprise stress testing, dynamic ALM, operational risk and CVA calculations
• Current best practice in risk-based performance management

 

 

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