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 has 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 in the new realities!

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This course is a part of the QuantUniversity Machine Learning and AI Risk Certificate Program. Avail additional discounts by enrolling in the Certification program.

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• Risk professionals
• Model Validators
• Model Auditors
• Data Scientists
• ML engineers and Software engineers involved in ML and AI deployment