Host: Val Srinivas, senior manager, Deloitte Services LP
Artificial intelligence (AI) and machine learning (ML) models hold great promise in transforming banking. However, lack of explainability prevents banks from taking advantage of the full power of AI. The emerging field of Explainable AI (XAI) aims to make AI models more intuitive and comprehensible without sacrificing prediction accuracy/performance. We’ll discuss:
- Considerations for implementing and scaling XAI across the enterprise
- Balancing the trade-off between model accuracy/performance and interpretability
- How to prioritize deployment of XAI across ML models
- Recommendations for integrating XAI into existing AI governance
Meet the speakers
Val Srinivas is the banking and capital markets research leader at the Deloitte Center for Financial Services. He leads the development of our thought leadership initiatives in the industry, coordinating our various research efforts and helping to differentiate Deloitte in the marketplace. He has more than 20 years of experience in research and marketing strategy.
Alexey is a Deloitte Risk & Financial Advisory partner leading the Model Risk Management team in Deloitte & Touche LLP. He works primarily in the areas of model risk management, quantitative modeling, valuation, and model validation. For more than 18 years he has assisted a number of clients on projects related to modeling and model validation, specializing in modeling and risk management of complex fixed income, mortgage, equity, foreign exchange, and credit products, as well as modeling of market, counterparty credit, liquidity, and operational risk at large financial institutions.