The use of AI and machine learning in finance has grown significantly in the last few years. As more and more AI and ML applications are being deployed in enterprises, concerns are growing about the increased complexity of models, the growing ecosystem of untested frameworks and products, potential for AI accidents, model and reputation risk. As the debate about explainability, fairness, bias, and privacy grows, there is increased attention to understanding how the models work and whether the models are designed and thoroughly tested to address potential issues.
The growth of data-driven applications have changed the financial industry. AI and ML models have accelerated business transformation, reduced turn-around times and have enabled applications that weren’t feasible just a few years ago. Institutions have ramped up the adoption of ML models and are seeing significant benefits through the growing portfolios of ML based decision making models. While the interest is huge, the challenges of comprehensively testing and evaluating ML models remain. AI accidents and the risks associated with algorithmic decision making is challenging enterprises to innovate and adopt risk management techniques factoring the new realities!
In this QuantUniversity Course, we will discuss the key aspects of risks in ML models and discuss key techniques in stress, scenario testing and evaluation of machine learning models. Through examples and case studies, we will discuss the state-of-the-art in testing and evaluation of ML-based models and how to comprehensively address risk when developing, deploying and monitoring ML applications. By the end of the course, participants will have a clear idea of the challenges, best practices and pragmatic tools that can be used to address risks in machine learning models
Hands-on examples and case studies through QuSandbox will be provided to reinforce concepts.