AI Innovation in Risk Management
No single approach to interpreting a neural network’s outputs is perfect, so it’s better to use them all.
As artificial intelligence becomes more powerful, explaining the outputs of these models also becomes more challenging. Deep learning techniques – and neural networks in particular – are playing an increasingly important role within financial institutions, where they are used to automate everything from options hedging to credit card lending.
Chris Dias, principal at KPMG, explains how the vast increase in accuracy that artificial intelligence (AI) offers when dealing with large volumes of complex agreements is crucial to exploring the market opportunities and mitigating the risks of the transition away from Libor. Implementing a robust AI capability is an important starting point.
Understanding exposure to Libor and the risk associated with it is a critical first step
Alex Jones, offering manager at IBM Watson OpenScale, charts the rise of artificial intelligence (AI) and machine learning in model development and discusses the challenges of these technologies in relation to model risk governance. Among the key issues Jones raises are the difficulties of explaining increasingly complex ‘black box’ algorithms, the risks of model bias due to unintentional correlations and how real-world data can present problems for outdated models.
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