AI/ML models are being extensively used in the financial industry for purposes ranging from Customer Sourcing, Credit Scoring, Campaign Management, Fraud Risk Management, Anti-Money Laundering, Rewards Management, Risk Management and Operations. Usage of ML models have made it possible to do tasks which otherwise would have been very cumbersome or humanly impossible.
However, there is a flip slide for the usage of ML models as well. For example, Machine Learning based models could suffer from serious flaws such as  bias, and explainability, if the models are not constructed, used, and governed properly.  

During the presentation, I intended to provide an overview of some of the best practices a Product Manager should adopt, throughout the lifecycle of AI Models, to mitigate AI Model Risk. As an agenda, I would be covering the following:

• AI Models: Usage in Fintech Products

• Key Functional & Technical Challenges for a Product Manager

• Key challenges of deploying models at scale: Indian context

• Key Regulatory Concerns: Bias and Explainability of AI Models

• Product Management across AI Model Lifecycle: Development |Deployment |Usage

• 3 Pillars for AI Models Product Management: Model Governance| Model Validation | Challenger Models

• References for further study