Date: 2nd November, 2023
Time: 7:00 pm IST
The lending landscape is undergoing a transformative shift. Today, AI/ML models enable financial institutions to make data-driven decisions, optimize risk assessment, and enhance customer experiences. However, managing associated risks with these technologies comes with its own set of challenges, and that's where observability steps in.
ML observability provides unparalleled transparency and ensures the reliability of AI-driven lending models. It becomes more crucial considering:
- Profitability and Quality Impact: Any failures or errors can significantly affect both the profitability and the overall quality of the book.
- Relevant Regulations: Various regulations, such as the AI Act (in the US and EU), GDPR (in the EU), and RBI Digital Guidelines (in India), provide clear guidelines and expectations from regulators regarding the use of AI.
- Bias Mitigation in Underwriting: The need to identify and address bias in underwriting has become of paramount importance.
- Auditability for Risk and Compliance: Ensuring auditability is crucial not only for effective risk management but also for maintaining regulatory compliance.
- Enhancing Underwriter Productivity: Transparency in models has the potential to significantly enhance the productivity of underwriters.
Join our workshop to get insights on how ML Observability tools can be used to design a framework for achieving a scalable and safe AI environment for the business. We will discuss:
- Overview of why ML observability is more crucial than ever
- Use case discussion: Designing ML observability strategy for Lending
- Details on necessary components of ML observability - Explainability, Monitoring and Model fine-tuning