New monitoring capabilities optimize model performance and enforce risk and compliance controls throughout the model life cycle
Five ways to mitigate the risk of AI models
Dave Trier, Global Banking & Finance Review – March 4, 2021
In recent years, the banking industry has been at the forefront of AI and ML adoption. A recent survey by Deloitte Insights shows 70% of all financial services firms use machine learning to manage cash flow, determine credit scores, and protect against cybercrime. According to an Economist Intelligence Unit adoption study, 54% of banks and financial institutions with more than 5,000 employees have adopted AI.
But AI and ML adoption has not been easy. Difficulty in deployment has been exacerbated by the growing number of new AI platforms, languages, frameworks, and hybrid compute infrastructure. Add to this the fact that models are being developed by staff in multiple business units and AI teams, making it difficult to ensure that the proper risk and regulatory controls and processes are enforced.