Algo teams with Agena to solve op risk data history issue

Agena's proprietary iRisk scorecard software uses Bayesian probabilistic modelling techniques to interpret assessment questionnaire results to predict future losses where there is little historical loss data available. The lack of an operational loss data history has proved a major concern to both bankers and regulators with regard to modelling operational risk. The Basel II capital Accord, due for implementation by 2007, will require financial institutions to assign regulatory capital to operational risk for the first time.

Quantitative results generated by iRisk can then be input into a bank's capital calculation using the Algo OpRisk Analytics module, which combines both qualitative and quantitative data. The synthesis of quantitative and qualitative data is viewed as an important factor in a firm's operational risk management practice and in the calculation of its operational risk capital requirements.

"Self assessment is increasingly coming under the spotlight as banks and regulators realise that the quantity and quality of loss data alone is unlikely to be sufficient for calculation of operational risk capital," said Michael Zerbs, chief operating officer at Algorithmics. "This is a significant partnership with Agena and enables Algorithmics to incorporate self-assessment scores into the capital calculation in a robust manner."

Agena was founded in 1998 and uses its own proprietary Bayesian network technology. Bayesian networks are viewed as the most powerful tool for measuring uncertainty.

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