Learning Objectives:

  • Describe the challenges of implementing AI in banking.

  • Explain the technology infrastructure required for AI.

  • Identify future trends in AI and data analytics.

8.1 Implementation Challenges

Implementing AI in banking requires addressing several challenges:

  • Data Quality: AI models require clean, accurate data.

  • Legacy Systems: Many banks struggle with outdated infrastructure.

  • Skills Gap: A shortage of skilled data scientists and AI engineers.

  • Cultural Resistance: Resistance to change from staff and management.

  • Regulatory Compliance: Navigating complex regulatory requirements.

8.2 Technology Infrastructure

AI requires a robust technology infrastructure :

  • Cloud Computing: Scalable, flexible infrastructure for AI workloads.

  • Data Management: Systems for collecting, storing, and processing data.

  • API Integration: Connecting AI models to banking systems.

  • Data Protection: Ensuring data is secure and compliant.

8.3 Future Trends

The future of AI in banking will be shaped by :

  • Hyper-Personalisation: Using AI to deliver tailored financial services.

  • AI-Powered Credit Products: Automated lending and credit scoring.

  • AI in Risk Management: Predictive analytics for emerging risks.

  • Voice Banking and Conversational AI: AI-driven customer interactions.

  • Embedded Finance: AI-powered financial services integrated into non-banking platforms.

  • Sustainable Banking: Using AI to assess ESG risks and opportunities.

The University of Edinburgh course covers how digitalisation, data-driven approaches, and innovative technologies are shaping the present and future landscape of banking . It also covers how banks integrate environmental, social, and governance (ESG) considerations into their business and operations

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