Subscribe for more and stay relevant
The Northern European newsletters deliver quarterly industry insights to help your business adapt, evolve, and respond—as if on intuition
Staying ahead in risk management in banking
Written by Cognizant GGM Consulting
12 December, 2024
Can generative AI help banks and financial institutions redefine their risk monitoring systems to act quickly to mitigate upcoming risks? While the industry increasingly recognizes the importance of early warning signals (EWS)as a risk management approach, it still relies on traditional models, as discussed in a new white paper.
Regulatory bodies like the European Banking Authority (EBA) and the UK Prudential Regulation Authority (PRA) urge banks to adopt proactive, forward-looking solutions for EWS. This is crucial for identifying potential risks and stress in financial institutions – before they escalate into larger issues. Banks are well aware of this.
A broader data set required
However, banks tend to rely on traditional models which predominantly rely on a rule-based monitoring of quantitative data, like earnings reports and credit ratings. These models often overlook the critical qualitative factors that can significantly influence borrowers’ risk trajectories. As a result, banks often tend to react rather than proactively plan.
What to do then? Integrating broader qualitative data in EWS – such as data on inflation, political climate, market trends, social media and environmental risks – is essential for a comprehensive risk analysis. These signals, influenced by quantitative and qualitative triggers, play a crucial role in identifying potential risks and enabling the implementation of preventive measures.
AI-infused risk management
An effective EWS monitors a range of qualitative, quantitative and market indicators to anticipate and quantify risks, where the integration of AI offers a transformative approach to analyze diverse data sources. By leveraging AI’s capabilities, banks can develop more nuanced and predictive early warning systems that go beyond conventional metrics. This modern approach enhances risk management and empowers banks to make more informed decisions, ultimately contributing to greater financial stability and resilience.
Learn more in the white paper “Beyond Defaults: Next-gen Early Warning System in the New Era”, which provides a step-by-step guide to adopting advanced methodologies and AI-driven models to stay ahead in risk management.
