Rewiring Banking for Gen AI: Unlocking Operational Efficiency in 2024.

Introduction

Generative AI (GenAI) is set to transform the banking industry by automating tasks, enhancing decision-making, and creating new operational efficiencies. A strategic approach to implementation and scaling is essential for banks to realise this potential.

Read on to discover how generative AI is revolutionising the banking industry by enhancing operational efficiency, streamlining financial processes, and fostering innovation. This blog provides a comprehensive guide on implementing and scaling AI initiatives, ensuring data security, and building a future-ready workforce—essential reading for any bank aiming to stay competitive in the digital era.

Strategic Focus Areas

Enhancing Internal Operational Efficiency

Banks can leverage GenAI to significantly improve internal processes, enhancing operational efficiency. Key areas include:

Improving the Balance Sheet

GenAI can significantly improve a bank's balance sheet by enhancing the understanding and managing risk-weighted assets (RWAs). By analysing more extensive sets of historical data and current market conditions, AI models can more accurately assess the risk associated with various asset classes. This detailed risk analysis allows banks to optimise their asset portfolios, reducing capital held against low-risk assets and freeing up capital for more productive uses. For instance, AI can identify and predict changes in asset risk profiles more quickly than traditional methods, enabling proactive adjustments that improve the overall financial health of the institution​.

Completing ESG/CSRD Disclosures

GenAI can streamline the completion of ESG (Environmental, Social, and Governance) and CSRD (Corporate Sustainability Reporting Directive) disclosures by automating the extraction of relevant data, ensuring it is formatted correctly, and generating comprehensive narratives. AI tools can gather data from disparate sources such as internal reports, external databases, and real-time data feeds. They then process this information to meet specific reporting standards and create coherent, accurate narratives highlighting the bank’s ESG efforts and compliance. This approach reduces the time and labour involved in producing these reports, ensures greater accuracy, and helps maintain regulatory compliance.

Automating Financial Planning Cycles

In financial planning, GenAI can automate and enhance the creation of rolling forecasts, continuously updated predictions that reflect the latest business and market conditions. AI integrates historical financial data with real-time operational data to create dynamic, predictive models. These rolling forecasts allow managers to make more accurate and timely adjustments to their financial plans, improving forecast accuracy and overall performance. For instance, AI can analyse trends and anomalies in real time, providing managers with actionable insights that help in strategic decision-making and resource allocation​​.

Automating Internal or Management Reporting

GenAI can transform the process of generating internal and management reports by quickly creating detailed reports, writing narratives, and linking relevant data from trusted sources. AI can pull data from verified internal systems, perform real-time analytics, and automatically generate comprehensive reports with insights into operational performance, financial status, and other vital metrics. This automation speeds up the reporting process, ensures accuracy, and provides a cohesive narrative that ties together various data points and reports. Managers can thus gain a holistic view of the business, uncovering insights that might otherwise remain hidden​.

Enhancing Manager Productivity with GPT-driven Insights

GenAI, particularly models like GPTs, can be trained on a bank's internal financial and statistical data to provide managers with powerful tools for improving productivity and gaining insights. Managers can interact with the AI through natural language queries to obtain detailed analyses, predictive insights, and data-driven recommendations. For instance, a manager might ask the GPT model to explain the factors driving recent changes in a specific financial metric, receive a comprehensive analysis, and get recommendations for potential actions. This capability enhances decision-making by providing instant access to sophisticated analytics and actionable insights.

Scaling AI Initiatives

Scaling GenAI from pilots to full-scale deployment requires robust infrastructure and a strategic approach:

• Data Quality: Ensuring high-quality data is crucial for the success of AI models. Implementing stringent data governance and cleansing protocols is essential.
• Integration Frameworks: Developing seamless integration frameworks allows easy data flow between AI models and existing banking systems, facilitating smoother AI implementation.
• Reusable Technology Components: Creating libraries of pre-approved tools and APIs can accelerate the deployment of AI applications across the organisation, promoting consistency and reducing development time.

Building Organisational Capabilities

To effectively implement GenAI, banks must invest in upskilling their workforce:

• Training Programs: Offering specialised training in AI technologies, including prompt engineering, bias detection, and fine-tuning large language models (LLMs), is essential.
• Apprenticeship Programs: Developing apprenticeship programs can help nurture AI talent within the organisation, ensuring a steady pipeline of skilled professionals.
• Communities: Building communities can facilitate knowledge sharing and collaboration among employees, promoting continuous learning and capability development.
  
  

The Ideal GenAI Cross-Functional Team Design

To build a successful GenAI initiative, consider creating a cross-functional team with these core skills:

1. DataOps: Manages and optimises the data pipeline, ensuring data availability and quality; supports training and deployment of AI models.
2. Reliability Engineer: Ensures software systems and applications' reliability, availability, and performance.
3. DevOps Engineer: Establishes CI/CD pipelines and other automation needed for rapid code development and deployment (e.g., chatbots, APIs) to production.
4. Cloud Architect: Ensures scalability, security, and cost optimisation of the cloud infrastructure; designs data storage and management systems; facilitates AI model integration and deployment.
5. Solution/Data Architect: Develop creative and efficient solutions using engineering practices and software/web development technologies.
6. Platform Owner: Acts like a product owner, overseeing the build of a next-gen AI platform.
   Full-Stack Developer: Writes clean and scalable code (e.g., front-end/back-end APIs) that can be easily deployed with CI/CD pipelines.
7. Data Scientist: Fine-tunes foundational models for RAG-based approaches; ensures alignment of LLM outputs with responsible AI guidelines.
8. Data Engineer: Architects data models for vector databases, maintains automated pipelines, performs testing to validate responses, and improves performance.
9. Business Analyst: The Business Analyst bridges the technical team and the business stakeholders. They are responsible for understanding the business needs, identifying challenges, and translating these into technical requirements that the platform team can work on.
   
   
   

Establishing Standards and Governance

A centralised governance team is critical for responsible AI use:

• Data Privacy and Security: Protecting sensitive data requires implementing robust security measures and adhering to regulatory requirements.
• Ethical Guidelines: Developing and enforcing guidelines to prevent bias and ensure fairness in AI-driven decisions is crucial for maintaining trust and credibility.
• Standardised Processes: Standardising AI development processes ensures consistency and quality across the organisation, facilitating smoother scaling and deployment.
  
  

Enhancing Data Management

Leveraging Unstructured Data

Banks have vast amounts of unstructured data, such as emails, voice recordings, and spreadsheets, which can be harnessed to improve AI models:

• Metadata Tagging: Organizing and classifying unstructured data with metadata tagging facilitates easier retrieval and analysis, enabling more effective use of this data in AI models.
• Data Augmentation: Enhancing training data with diverse and enriched datasets, including information from spreadsheets, improves model accuracy and robustness.
  
  

Optimising Data Infrastructure

Optimising data storage and processing is crucial for cost efficiency and effectiveness:

• Prioritising Data Availability: Ensuring critical data is readily accessible based on usage needs helps optimise operational efficiency.
• Cloud Resources: Leveraging cloud infrastructure allows for scaling dynamic data storage and processing capabilities, reducing infrastructure costs and improving flexibility.
  
  

Creating a Culture of Trust and Reusability

Building Trust in AI Tools

Transparency and explainability are crucial to gaining user trust in AI tools:

• Explainable AI: Developing models that clearly explain their decisions helps build trust among users and stakeholders.
• Policy Linkages: Linking AI decisions to relevant policy documents and sources enhances credibility. For instance, providing links between critical decisions and the AI sources used ensures transparency and accountability.

Promoting Reusability

Encouraging the reuse of AI components can drive efficiency and innovation:

• Standards for AI Development: Establishing standards for AI asset development ensures they are reusable and interoperable, promoting consistency and efficiency.
• Culture of Reusability: Incentivising the sharing of AI components and best practices fosters a collaborative environment and accelerates innovation.
  
  

Challenges and Considerations

Data Privacy and Security

Ensuring data privacy and security is paramount:

• Robust Security Measures: Implementing advanced encryption and access controls protects sensitive information from breaches and unauthorised access.
• Regulatory Compliance: Adhering to industry regulations and standards ensures data privacy and security, fostering trust among customers and stakeholders.

Ethical and Bias Concerns

Addressing bias and ensuring ethical AI practices is critical:

• Bias Monitoring: Continuously monitoring AI models for bias and implementing corrective measures as needed ensures fairness and accuracy.
• Ethical AI Practices: Adopting ethical AI practices, including transparency, accountability, and fairness, helps maintain trust and credibility.
  
  

Conclusion

Generative AI offers immense potential for transforming the banking industry by enhancing operational efficiency, improving decision-making, and driving innovation.

Banks can unlock significant value and drive business growth by strategically implementing GenAI, investing in workforce upskilling, optimising data management, and fostering a culture of trust and reusability. As the financial landscape evolves, banks that successfully integrate GenAI will be well-positioned to lead in the era of digital transformation.

How can we help?

Revvence can help in several valuable ways:

• Review your existing core finance processes and recommend GenAI use cases.
• Create a proof-of-concept to demonstrate potential outcomes and help build your business case.
• The design and delivery of end-to-end GenAI applications in the finance function.
• Check out Revvy, our Narrow-GPT for Finance Transformation. Read all about Revvy here.