We provide an estimate of losses due to cyber events, a subset of operational loss events. Better supervision, on the other hand, is associated with lower operational losses. In particular, we show that credit booms and periods of excessively accommodative monetary policy are followed by larger operational losses. Operational losses are not independent of macroeconomic conditions and regulatory characteristics. For instance, improper business practices and internal fraud events take longer to be discovered. However, there is significant heterogeneity across regions and event types. It takes, on average, more than a year for operational losses to be discovered and recognised in the books. Operational value-at-risk can vary substantially - from 6% to 12% of total gross income - depending on the method used. The spike is largely accounted for by losses due to improper business practices in large banks that occurred in the run-up to the crisis but were recognised only later. After a spike following the great financial crisis, operational losses have declined in recent years. We use a unique cross-country dataset at the loss event level to document the evolution and characteristics of banks' operational risk. Cyber losses are still a small portion of operational losses, but can account for a significant share of total operational value-at-risk. In particular, the paper shows that credit booms and periods of excessively accommodative monetary policy are followed by larger operational losses. However, there is significant variation across regions and event types. Operational value-at-risk can vary substantially across banks - from 6% to 12% of total gross income - depending on the method used. The spike was largely due to losses arising from improper business practices in large banks that were incurred in the run-up to the crisis but recognised only later. FindingsĪfter a spike following the Great Financial Crisis, operational losses have fallen in recent years. The granularity of the data set allows us to study the evolution of operational risks through time, compute an operational and cyber value-at-risk for financial intermediaries, document the time lag between occurrence, discovery and recognition of losses, and investigate the link between operational losses, macroeconomic conditions and regulatory characteristics. The sample contains over 700,000 operational loss events from 2002 until end-2017 for a group of 74 large banks with headquarters worldwide. We use a unique cross-country data set from ORX, a consortium of financial institutions. Measuring and understanding operational risks, including cyber risks, is critical for both banks and public authorities. Representing a significant portion of total bank risks, operational risks are currently second only to credit risks as a source of losses. Operational risks are related to losses that could result from inadequate or failed internal processes, improper business practices, systems failures or from external events.
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