The transition to the new reference rates is the single largest data reconfiguration endeavor that financial institutions have been faced with since Y2K, and on a stretched timeline. Financial Institutions must effectively identify LIBOR data in order to perform a comprehensive evaluation of risk and exposure. This includes contractual risk, financial risk and risks to operations and systems. Additionally, financial institutions need to determine a set of actions for their businesses, operations and clients to effectively operationalize to the transition to alternative reference rates.
This paper explores the data challenges associated with the transition and how AI and automated metadata harvesting can efficiently address these challenges.
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