5 Model Risk Management Lessons from Early Adopters of Machine Learning
MRM best practices from 30 leading banks, insurers, and fintechs
As the adoption of machine learning ramps up in financial services, one group that finds itself in an increasingly critical position is Model Risk Management (MRM). MRM teams are expected to deal with a huge increase in the number of models; deal with technical concerns such as explainability, unfair bias and overfitting; and be faster and more efficient at the same time!
TruEra has worked closely with over 30 leading banks, insurers and fintech firms and seen first hand how MRM teams are responding to the challenges of burgeoning machine learning models. The lessons learned are vital for everyone in any industry that is deploying machine learning today.
Read this whitepaper and you will learn:
- How financial services’ practice of model risk management has become central to scaling up the adoption of machine learning
- The most common MRM challenges in the era of machine learning
- 5 best practices from early adopters of machine learning in the industry
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Read the whitepaper
TruEra provides AI Quality solutions that analyze machine learning, drive model quality improvements, and build trust. Powered by enterprise-class Artificial Intelligence (AI) Explainability technology based on six years of research at Carnegie Mellon University, TruEra’s suite of solutions provides much-needed model transparency and analytics that drive high model quality and overall acceptance, address unfair bias, and ensure governance and compliance.