# Banking Industry GenAI Implementation Case Study ## Overview This case study examines the implementation of generative AI technologies at major financial institutions, primarily focusing on Deutsche Bank's experience and insights from other banking leaders. The discussion provides valuable insights into how large regulated financial institutions are approaching LLMOps and generative AI implementation. ## Key Implementation Areas ### Document Processing and Analysis - Implemented Doc AI service provided by Google Cloud - Enhanced basic service to make it "fit for banking" with necessary controls - Focused on processing hundreds of thousands of unstructured documents daily - Moved beyond traditional OCR to more sophisticated AI-based processing - Adopted service-oriented architecture approach with "concept of one" to avoid duplicate capabilities ### Research and Content Management - Applied to Deutsche Bank research department - Automated 80% of data collection work previously done manually - Enhanced ability to produce ad-hoc research reports - Improved response time to market events - Enabled more dynamic and responsive customer advice ### Risk Modeling and Assessment - Enhanced risk models with expanded data processing capabilities - Improved analysis of market conditions and customer behavior - Leveraged cloud computing power for more detailed risk assessment - Working closely with Chief Risk Office to expand model capabilities ## Implementation Strategy and Governance ### Project Selection Approach - Used grassroots approach to collect use cases - Clustered use cases into categories - Validated applicability across multiple application areas - Started with internal efficiency gains rather than customer-facing applications - Focus on augmenting human capabilities rather than replacement ### Regulatory and Compliance Considerations - Implemented under model risk management framework - Regular engagement with regulators about implementation plans - Focus on controlled, proven use cases - Avoidance of customer-facing activities initially - Strong emphasis on responsible AI principles - Consideration of data residency requirements - Management of cross-jurisdictional regulatory requirements ### Change Management and Culture - Strong executive sponsorship required - Monthly updates to CEO level - Establishment of dedicated Center of Excellence - Integration of business partners into advisory groups - Focus on employee upskilling and reskilling - Management of cultural transformation ## Technical Implementation Details ### Architecture Principles - Service-oriented architecture - Centralized services to avoid duplication - Integration with cloud infrastructure - Focus on scalability and reusability - Built-in controls and governance ### Development and Testing - Enhanced engineering productivity - Improved test coverage and automation - Integration with existing development tools - Focus on code quality and robustness ## Challenges and Lessons Learned ### Regulatory Challenges - Need for constant dialogue with regulators - Balance between innovation and compliance - Management of data residency requirements - Concern about potential regulatory reactions to industry incidents ### Implementation Challenges - Managing expectations around cost savings - Balancing speed of implementation with control requirements - Ensuring business case clarity - Managing rapid technology changes ### Risk Management - Focus on bias prevention - Management of hallucination risks - Implementation of appropriate controls - Protection of customer data - Maintenance of trust as primary concern ## Future Outlook ### Expected Developments - Movement toward more customer-facing applications - Enhanced personalization capabilities - Improved operational efficiencies - New business model enablement - Continued focus on responsible implementation ### Strategic Considerations - Need to maintain competitive position - Balance between innovation and risk - Importance of staying current with technology - Management of geopolitical constraints ## Results and Impact ### Current Achievements - Successful implementation of document processing systems - Enhanced research capabilities - Improved risk modeling - Increased operational efficiency - Positive employee engagement ### Measured Benefits - Reduction in manual document processing - Improved research output capabilities - Enhanced risk assessment capabilities - Better regulatory compliance management - Increased operational efficiency ## Key Success Factors - Strong executive sponsorship - Clear governance framework