22 D Artificial intelligence
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What is Artificial Intelligence (AI) in the context of banking?
AI in banking refers to the use of machine learning, natural language processing, and related technologies to automate decision-making, detect patterns, and enhance financial services delivery.
What is 'machine learning' and how does it differ from traditional programming in banking?
ML learns from data patterns without explicit rule programming.
Which RBI guideline provides the primary regulatory framework for the use of AI and ML in Indian banking?
RBI's Master Direction on IT Governance, Risk, Controls and Assurance Practices (2023) and its guidelines on digital lending and model risk management collectively govern AI use in Indian banks.
What is 'neural network' architecture and what banking tasks does it perform best?
Layered nodes mimicking brain; best for image recognition and fraud detection.
What is 'Explainable AI' (XAI) and why is it important for banks?
Explainable AI refers to AI systems whose decisions can be interpreted and understood by humans; it is critical in banking to ensure transparency, regulatory compliance, and fair treatment of customers, especially in credit decisions.
What is 'reinforcement learning' and which banking application leverages it most?
Agent learns by reward signals; used in algorithmic trading strategies.
What is model risk in the context of AI-driven banking decisions?
Model risk is the risk of adverse consequences arising from errors in the development, implementation, or use of AI/ML models, including incorrect predictions, biased outputs, or model failure under changed conditions.
What is 'federated learning' and why is it valuable for banks sharing fraud data?
Models trained locally without sharing raw customer data across institutions.
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