Machine learning governance - Risk.net

The ability of machine learning models to read great quantities of unstructured data, spot patterns and translate it into actionable information is driving a significant uptake in the technology. Today, there is great interest in harnessing machine learning to turn the massive volumes of data – including non-traditional data – into new insights and information. In contrast to traditional statistical models, which are limited in the number of dimensions they can effectively access, machine learning models overcome these limitations and can ingest vast amounts of unstructured data, identify patterns and translate them into actionable information. It is therefore no surprise that machine learning modelling is being eagerly adopted. A recent survey conducted by SAS and the Global Association of Risk Professionals found that, over the next three to five years, businesses expect to significantly increase adoption of artificial intelligence (AI) and machine learning models to support key risk business use cases (see figure 1).