Heavy Reliance on AI could pose risks in Financial Sector: RBI Governor | Current Affairs | Vision IAS
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Speaking at the 90th High-Level Conference organized by the RBI in New Delhi, Governor expressed concern that the growing use of AI could enable a few technology providers to dominate the market, creating systemic vulnerabilities.

  • Currently, AI in Financial System is being used in Algorithmic and high-frequency trading, credit scoring and approvals, customer services through instruments like chatbots, predictive analytics for market trends for risk management, etc.

Risks posed by AI to banking and financial services

  • Concentration risks: If many financial institutions use similar AI models for trading or risk assessment, a failure or error in these algorithms can have cascading effects across global financial markets.
    • e.g., AI trading systems can amplify market volatility by triggering mass sell-offs during downturns.
  • Algorithmic biases: AI systems are trained on historical data can lead to unfair practices like discriminatory lending or credit decisions. 
    • e.g., An AI-driven loan approval system may inadvertently deny loans to specific demographic groups.
  • Data security and privacy: Breach or misuse of data can lead to identity theft, fraud, and significant losses for both institutions and customers.
  • Others: Lack of transparency due to ‘Black Box’ problem, misleading information due to ‘AI hallucinations’, etc.

Measures to be taken to address these risks

  • Comprehensive AI Regulation: By engaging in industry-wide collaboration with researchers, security experts and policymakers.
    • Learning from global best practices such as Algorithmic Accountability Act of 2023 of the US.
  • Maximize Defense Capabilities: Adopt ‘Security by design’ approach to incorporate robust security features at every stage of AI development lifecycle ensuring the foundational integrity of AI systems.
  • Learning and adaptation: Advanced threat detection through analysis of vast data sets in real time to uncover patterns and anomalies indicative of cyber threats.
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