As the efficiency of operational risk management remains a top priority and pressure to maximise value increases, emerging technology could prove crucial. Nitish Idnani, leader of oprisk management services at Deloitte, explores how the oprisk management space could look in the future if it continues its current evolution, and discusses the potential impact of key technologies

The efficacy and efficiency of operational risk management continue to be a major priority in today’s business climate. The ability to demonstrate the value of oprisk management frameworks – with risk managers being increasingly expected to do more with less – is increasing. This pressure is creating an incentive for risk leaders to explore and embrace new technologies and techniques that can help improve their programmes.

Predictive risk intelligence and the use of advanced analytics for pattern recognition, and correlation and causal analysis give oprisk managers a head start on identifying the build-up of potential risk and the need for remedial action. Banks should seize the opportunities made possible by today’s advanced tools and the ubiquity of vast amounts of data. 

Predictive risk analytics, machine learning and artificial intelligence can help efficiently build and mine large and complex datasets that combine traditional Basel Committee on Banking Supervision oprisk loss data with other data sources, including transaction data; non-transaction data such as human resources, compliance and other internal management information; and external data such as sensing data, social media, customer complaints and regulatory actions. These aggregated datasets provide billions of data combinations that can drive improved risk results and insights, and may increase the likelihood of uncovering patterns and correlations that were previously not noticed until too late, if at all. 

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