By: Rajosik Banerjee
In a new era of unprecedented customer experiences, the demand for faster and high-quality financial analysis and forecasting has increased significantly. Financial institutions, globally as well as in India, are using supervised and unsupervised Artificial Intelligence (AI) and Machine Learning (ML) models to manage risks or to calculate prices.
However, they always run a risk of an incorrect model specification or an incorrect use of a model leading to a decision, having material consequences, such as financial and reputational losses, commonly referred to as Model Risk.
Model Risk Management
To measure and mitigate Model Risk, institutions are now having to look at establishing extensive and complex Model Risk Management (MRM) structures. Additionally, with enhanced customer service and mounting cost pressures, they are also having to look at AI and ML as possible solutions for improving cost and operational efficiencies. The benefits of adopting AI/ML models are too powerful to ignore. But at the same time, it is important to have an international regulatory guidance on how to handle specific risks arising out of these models.
Let’s look at the seven…
