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Machine learning in risk management

Machine learning (ML) models have already been around for decades. The exponential growth in computing power and data availability, however, has resulted in many new opportunities for ML models. One possible application is to use them in financial institutions’ risk management. This article gives a brief introduction of ML models, followed by the most promising opportunities for using ML models in financial risk management.

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Quantification of model risk

Risk management and treasury specialists are using diverse models on a daily basis to manage various risks. It is easy to forget about the risk that is implied in using the model itself. What we refer to as ‘model risk’ can arise due to misuse of the model, incorrect model choices or inappropriate model use.

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Zanders Model Risk Framework

As automation and digitalization are adopted more widely in the financial industry, the number of financial models used is also steadily growing. As a result, an institution’s success or failure depends increasingly on the accuracy and reliability of those models.

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