Oracle AI Solutions for Modern Banking Operations.

As global banks continue to invest in digital transformation, Oracle's Artificial Intelligence (AI) and Generative AI (GenAI) offerings, showcased at this year's Oracle CloudWorld, present a rich toolkit designed for large-scale, enterprise-level AI-powered applications and agents. With a focus on security, scalability, and real-time data processing, Oracle's AI platform is designed to meet the complex demands of the banking sector.

This blog delves into Oracle's AI and Generative AI (GenAI) capabilities, including a detailed exploration of Retrieval-Augmented Generation (RAG) and the potential of AI Agents. We aim to share insights into how these technologies can be deployed effectively within our typical banking client landscape.

Disclosure: This blog was created based on a conversation (or series of prompts) with Revvy, a ChatGPT developed by Revvence and trained on finance transformation and systems change-related content.

Oracle Artificial Intelligence Solutions Overview

Oracle's AI platform offers a fully integrated ecosystem that supports every stage of the AI lifecycle, from data integration, ingestion, and preparation to model training and deployment. Here's a high-level look at the key AI offerings:

Oracle Cloud Infrastructure (OCI) Data Science Platform:

Oracle Cloud Infrastructure (OCI) Data Science is a cloud platform for developing and deploying machine learning models. It integrates seamlessly with Oracle Autonomous Database, enabling real-time analytics without data movement—a critical feature for banks prioritising data security and compliance. The platform supports popular frameworks such as TensorFlow, PyTorch, and scikit-learn, allowing in-house data scientists to leverage familiar tools while benefiting from Oracle's enterprise-grade infrastructure.

Oracle AI Infrastructure

Oracle AI Infrastructure is a high-performance AI infrastructure, including GPU and CPU instances optimised for large-scale AI workloads. Oracle offers a Supercluster which can scale up to 32,768 NVIDIA Blackwell GPUs today and 131,072 GPUs soon (you can read about this announcement at this year's Oracle Cloudworld here). This infrastructure is crucial for training large language models (LLMs) and running real-time analytics across vast datasets, such as for stress testing. Banks can scale their AI capabilities efficiently, ensuring they can meet the demands of stress testing, fraud detection, and other resource-intensive workflows.

Oracle Generative AI

Oracle's GenAI services offer powerful tools for automating content creation, enhancing decision-making processes, and improving customer engagement. The GenAI platform includes pre-trained models that can be fine-tuned for specific banking applications, such as generating regulatory reports or automating customer service interactions.

The Oracle GenAI product supports multiple LLMs, including Cohere and Meta Lama 3. We will deep dive into GenAI in a separate post, but there are a few benefits to banks of the Oracle approach:

1. Choice of models for enterprise use cases
   Banks can access pre-trained, foundational models from our Cohere or Lama to summarise, embed, and generate text across multiple use cases.
2. Flexible fine-tuning
   Banks can create custom models by fine-tuning base models with their own data set, building new custom models, or creating new versions of existing ones.
3. Content moderation and controls and dedicated AI clusters
   Lockdown how content is added, shared or used and create internal AI clusters to maximise security and privacy.

Oracle GenAI services can be easily integrated into existing applications and power multiple chatbots across the organisation.

Oracle's Generative AI (GenAI) capabilities are designed to automate and enhance complex tasks like regulatory reporting and ESG (Environmental, Social, and Governance) analysis. These areas are increasingly critical for banks, which must manage compliance with ever-changing regulations while demonstrating their commitment to sustainable and ethical practices.

Regulatory Reporting with GenAI:

Oracle's GenAI tools can be employed to automate the generation of regulatory reports by integrating data from various sources, including existing Oracle applications and non-Oracle data, such as AWS-hosted datasets. This ensures that reports are accurate, timely, and aligned with the latest regulatory requirements. For example, Oracle's AI can automate the production of Basel IV compliance reports, pulling data from disparate systems and ensuring it is formatted according to regulatory standards.

ESG Analytics and Reporting:

As banks increasingly prioritise sustainability, Oracle's AI tools can analyse large datasets related to environmental impact, social governance, and other ESG factors. By automating the data analysis and reporting processes, Oracle's GenAI can help banks create comprehensive and compliant ESG reports, meeting both investor demands and regulatory requirements.

Oracle Retrieval-Augmented Generation (RAG)

One of Oracle's standout capabilities in the AI space is its integration of Retrieval-Augmented Generation (RAG) within its GenAI framework. RAG combines the strengths of LLMs with real-time data retrieval, ensuring that AI outputs are accurate and grounded in the most current and relevant data.

How could RAG work in Banking?

RAG can be deployed in banking to enhance fraud detection, customer support, and regulatory reporting tasks. By integrating Oracle's AI models with live data sources—whether stored in Oracle Autonomous Database, AWS, or other platforms—banks can generate real-time insights and responses that are contextually relevant and highly accurate.

For example, a RAG-enabled system could analyse a customer's transaction history, provide tailored recommendations, or flag suspicious activity while referencing the latest available data.

RAG applications can be deployed to help with internal compliance checks, quickly cross-check current lending conditions policies, or automate internal management reporting generation.

Deployment in a Secure Environment:

Oracle allows banks to deploy RAG models within their private cloud environments, ensuring that all AI operations comply with internal security protocols and regulatory standards. This private deployment capability is a crucial differentiator, offering banks the flexibility to maintain control over their data while leveraging advanced AI technologies.

Oracle AI Agents

A particularly exciting development within Oracle's AI suite, showcased at this year's Cloudworld, is the use of AI Agents powered by GenAI. These agents can automate complex tasks, streamline operations, and enhance customer interactions, all while working within the secure confines of a bank's existing infrastructure.

What Are AI Agents?

AI Agents are essentially AI-driven assistants that can handle complex interactions by combining large language models (LLMs) capabilities with real-time data retrieval (RAG). These agents can be deployed across various channels, including chatbots, virtual assistants, and even back-office operations, to automate tasks that would otherwise require human intervention.

Use Case: AI Agents in Customer Support:

For example, a global bank could deploy AI Agents to manage customer service inquiries. These agents could handle routine queries, such as account balance requests or transaction histories while addressing more complex issues by pulling real-time data from internal systems. The integration of RAG ensures that responses are accurate, contextually relevant, and up-to-date.

Use Case: Operational Efficiency:

Beyond customer service, AI Agents can be used to automate internal processes, such as compliance checks, risk assessments, and even HR functions. By leveraging Oracle's AI infrastructure, these agents can process large volumes of data quickly and accurately, reducing the operational load on human teams and allowing for more strategic focus on high-level tasks.

Integration and Security:

Oracle's AI Agents can be deployed within a bank's private cloud, ensuring that all interactions and data processes comply with the bank's stringent security and regulatory standards. This private deployment capability makes Oracle's AI Agents particularly attractive for banks that handle sensitive financial data and must maintain strict control over their AI operations.

Oracle AI Security & Compliance

Security and privacy are paramount in the banking sector, and Oracle's AI platform is designed with these critical factors at its core. Oracle's AI offerings are built on Oracle Cloud Infrastructure (OCI), which provides enterprise-grade security features essential for managing sensitive financial data.

1. End-to-End Encryption: Oracle uses industry-standard encryption protocols to ensure that data is encrypted at rest and in transit. This is particularly important for banks, where protecting customer information and transactional data is a top priority. Oracle's encryption capabilities ensure that even if data is intercepted, it remains inaccessible to unauthorised parties.
2. Compliance with Global Regulations: Oracle's AI platform is compliant with major regulatory frameworks, such as GDPR CCPA, and financial regulations like Basel IV. This compliance ensures that banks can deploy AI solutions while adhering to global data protection and privacy standards.
3. Dedicated Security Features: Oracle's AI services come with advanced security features, such as Identity and Access Management (IAM), which allows banks to control who has access to specific data and AI models. Oracle's AI infrastructure is also regularly audited and certified to meet the highest security standards, ensuring that banks can trust the platform with their most sensitive data.
4. Isolation and Data Sovereignty: For banks that require strict data sovereignty, Oracle offers the ability to deploy AI services in isolated environments. This ensures that data remains within specific geographic regions and is subject to local regulations. This capability is critical for global banks that operate across multiple jurisdictions.

Oracle AI Solutions - What the Analysts Say

Independent analysts have recognised Oracle's AI strategy as a strong contender in the enterprise AI space, particularly for large organisations like global banks.

Gartner's Perspective:

Gartner has consistently placed Oracle as a leader in the cloud infrastructure space, noting its strong AI and machine learning capabilities. Oracle's focus on integrating AI across its enterprise applications and its robust infrastructure positions it as a key player in the AI market. Gartner highlights Oracle's ability to deliver AI solutions that are not only powerful but also scalable and secure—key requirements for large banks.

Forrester and IDC Views:

Forrester and IDC have also praised Oracle's AI strategy, particularly its emphasis on security, integration, and industry-specific solutions. Oracle's AI tools, especially when integrated with its cloud infrastructure, allow banks to manage vast amounts of data securely and efficiently. Analysts have noted that Oracle's approach to embedding AI directly into its enterprise applications makes it a compelling option for organisations that require robust, scalable AI solutions that can be deployed quickly and securely across global operations.

The Futurum Group:

According to The Futurum Group, Oracle's AI approach is distinguished by its enterprise focus, especially in the financial sector. Oracle's ability to integrate AI directly into its SaaS applications, such as those used for financial services, makes it a powerful solution for banks looking to leverage AI without overhauling their existing systems. This focus on integrating AI across the stack—from infrastructure to applications—has been highlighted as a key strength of Oracle's strategy.

Get Started with Oracle AI Solutions

Oracle's AI and GenAI offerings provide a powerful, secure, and scalable platform that enables any bank to innovate while maintaining the highest security and compliance standards.

Many of our clients have Oracle, Google, SAP, AWS, and Microsoft, creating complex standardisation decisions and data integration challenges. However, Oracle's solutions and advanced AI capabilities are ready to deploy and can integrate with existing cloud applications like AWS and SAP; in fact, Oracle just announced a strategic partnership with AWS, which you can read about here. We recommend our clients select the best AI platform based on their user processes. For example, Oracle's EPM Cloud and Oracle Fusion Cloud applications are widely used across the banking industry, so exploring the Oracle capabilities now embedded in these applications makes sense.

Independent analysts like Gartner recognise Oracle's strength in enterprise AI, especially in the financial sector. With its focus on security, privacy, and managing sensitive banking data, Oracle's AI platform is uniquely positioned to support the complex needs of global banks.

Talk to Revvence today to explore Oracle AI solution use cases or to run pilot projects to validate a transformation initiative or business case.

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 ChatGPT for Finance Transformation. Read all about Revvy here.