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Unlocking the Potential: Machine Learning in the Context of IRB Internal Modelsm - Michał Opalski / ai-agile.org

In the realm of finance, where risk management is paramount, the evolution of technology has introduced new tools and methodologies to assess and mitigate risks. One such advancement that has garnered significant attention is the integration of machine learning into Internal Ratings-Based (IRB) models. As financial institutions navigate the complexities of risk assessment and regulatory compliance, the adoption of machine learning holds promise for enhancing accuracy, efficiency, and ultimately, the stability of the financial system. At its core, an IRB framework allows banks to determine regulatory capital requirements based on their internal risk assessments, rather than relying solely on standardized approaches. This flexibility enables banks to tailor risk measurements to their specific portfolios, potentially resulting in more accurate capital allocation. However, the effectiveness of IRB models hinges on the accuracy of risk assessments, which can be challenging to achieve using

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