In a financial institution, everyone is subject to managing risks, for instance, business functions (the first defense line–FLoD) own and operate their risks through processes and controls, second defense line–SLoD is concerned with risk management insights, oversight, and challenges. Second defense line-SLoD, where AI can be deployed across defense lines, cover different risk management activities.
As Artificial Intelligence evolves from mundane and repetitive tasks to more complex pattern recognition for decision making, there are many advantages of deploying it in the risk domain. Deployment of AI in BFSI brings in the speed of delivery, the integration of multiple systems for data collection and analysis, and the consistent provision of insights with the reduced human bias to risk managers.
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AI can be used to gather valuable insights to better understand the risk. AI in risk monitoring can provide frameworks, standards, and controls within which risk activities can be carried out. It can be used to inspect specific risk-taking aspects to ensure compliance with specified frameworks and standards, highlighting scrutiny exceptions, including those in business AI facilities. Once AI has become stable and mature, it can be used to offer an independent view of crucial risk decisions and challenge the FLoD independently. For example, AI can be used to process data patterns to identify new risks and to alert risk managers in a variety of areas, such as financial crime transaction screening, customer due diligence or real-time credit appetite based on information processed from commodity prices and currency variations.
AI’s success depends on the relevance and quality of the data sources it contains. However, if AI is found on the faulty data or programs, it can result in perverse results, potentially triggering severe consequences. If companies and banks get it right, AI can transform risk management with incisive analytics and cognitive computing beyond the realm of human capacity.