The desire among financial institutions to better mitigate risk gained renewed prominence as a result of the financial crisis of 2008. Subsequent regulatory and governance requirements fostered interest in risk modeling and sophisticated forecasting based on artificial intelligence (AI) to improve outcomes.
It now seems common to have AI-driven models supporting decision making related to capital adequacy, liquidity, pricing, exposure and more. Model risk management (MRM) also emerged as a practice, one that is ideally suited to the application of explainability to enable transparency, support governance and facilitate compliance.
Financial institutions have access to more datasets and computing resources than ever before, so they are increasingly adopting modern AI systems, including machine learning (ML) and deep learning (DL), to exploit these assets and capabilities. Yet these same institutions must also contend with the inherent risks posed by the resulting models themselves. Leveraging hindsight and insight to simulate foresight creates unavoidable risk that must be mitigated, especially when using modern AI-based systems that are a generation ahead of traditional rules-based ones.
Read the full article from Fiddler’s CPO, Amit Paka, on Forbes.