For instance, banks are utilising ML predictive models in the financial sector to analyse a vast range of interconnected metrics in order to better understand and serve consumer needs. Additionally, risk exposure can be found and limited using Machine Learning prediction models. Banks are better able to estimate risk for new products, follow and document fraudulent consumer activity, and spot cyber threats. The top use cases for ML in banking include credit score and loan analysis, fraud detection and mitigation, and personal financial advisor services.
Businesses in the industrial sector have embraced automation and are increasingly instrumenting both processes and equipment. They reorganise and optimise production using Machine Learning modelling in a way that is both responsive to the demand of the moment and aware of potential future change. A manufacturing method that is both flexible and durable is the end result. Yield improvements, root cause analysis, and supply chain and inventory management were the top three ML use cases in manufacturing.
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