Risk Modeling

CHAPTER 3Artificial Intelligence, Machine Learning, and Deep Learning Models for Risk Management

Despite the hype of the last few years, artificial intelligence (AI) and machine learning have proven themselves as useful tools in risk management. As stated earlier, the main reasons are the availability of vast amounts of digital data, greater computing power, and easier access to complex analytics via available modeling tools. More recently, both adoption of digital channels and increase in generated data have accelerated due to the COVID-19 pandemic. This came at a time when financial institutions were already facing myriad internal and external demands:

• Regulatory demands—compliance to new waves of prudential and financial reporting standards
• Macroeconomic demands—sustained low interest rates and inefficiencies putting pressure on profitability and increasing costs, due in part to the inflated cost of compliance
• Technological advancements—advanced analytics, big data, open banking, cloud-based and high-performance computing
• Emerging risks—cyberattacks, those created by the COVID-19 pandemic, climate change, and geopolitical uncertainty
• Digital transformation—challenges to the traditional banking model from new customer demands and the rise of newer, more nimble financial technology companies

Although not without challenges of their own, the use of AI and machine learning is one effective way that organizations can improve their agility to respond to these demands. Some of ...