Operational Risk and Artificial Intelligence: Opportunities for improved risk management
Risk management is an integral part of banking. By taking financial risks, banks are able to generate the profits that are necessary to survive. Risk management aims to control this process by making losses more predictable. This makes the bank more robust to external fluctuations.
Whereas profits can be made by accepting certain financial risks, operational risk is intrinsically different. Operational risks only cause losses, both in the financial sense as in different forms such as reputational damage. The consequences of operational risk events can have a large impact on an organization and the financial system as a whole as experienced during the last financial crisis. It logically follows that banks try to minimize the operational risk they take, given the resources available and restricted by the strategic goals of the organization. It is therefore not surprising that operational risk has received more attention within the financial sector, especially since the global financial crisis.
In the past decade, there has been major progress in the development of artificial intelligence (AI). AI algorithms excel at data analysis and have evolved to the point where they surpass human performance at a wide variety of tasks. More and more businesses exploit these technological advances to optimize different kinds of processes such as advertisement, marketing and logistics. In a world that becomes more and more data driven it is to be expected that this trend will continue. Can AI solutions contribute to the field of operational risk? What challenges does operational risk face and how might computers assist us and improve risk management?