Machine learning (ML) techniques are creating waves within the financial services sector. The banking industry, which relies heavily on data, is increasingly adopting these techniques and has started to leverage their powerful capabilities.
From chatbots to fraud detection, the banking sector is using ML not only to automate processes and streamline operations for both the front and back offices, but also to enhance the overall customer experience. ML tools, with their advanced prediction techniques and capabilities to utilize large volumes of data, are increasingly being used in risk management. They may inform quicker and more efficient credit, investment and business-related decision making.
Another important area where ML is gaining significance, albeit at a slower pace, is regulatory stress testing. Traditional statistical methods of stress testing have been critiqued by investors and regulatory agencies as ‘not severe enough’, with numerous banks failing during crises.
In the following pages we discuss the application of ML in risk management, as well the benefits and challenges of adoption.