Leveraging AIMLUX.AI components and IBM Data Unification

 

Webinar -Leveraging Equituis.us KGNN components and IBM Data Unification

To enhance it's position as one of the World's premier Banking Enterprise MUFG can investigate the strategic opportunities with Global Ai Deployment. In major international banking system upgrade leveraging AIMLUX.AI components and IBM data unification layers is a complex undertaking that must balance innovation with security and stability. The process follows a structured, multi-phase approach, culminating in a highly efficient, AI-driven enterprise.

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 AIMLUX.AI components are:  Graph, Model and Secure 

• PowerGraph (Equitus.us): The Knowledge Graph Neural Network (KGNN) for advanced relationship modeling and data unification.

• Plane Control (Wallaroo.ai Option Plane): The MLOps platform for high-scale, low-latency AI model deployment and governance.

• Wingman (Cyberspatia.com): A proxy for an AI-powered system resilience and security monitoring tool.

  

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Phase 1: Planning and Data Unification Foundation

This phase establishes the secure, performant foundation using IBM data unification layers, typically based on an IBM Data Fabric architecture, often utilizing IBM Power 10/11 hardware for its security and resilience.

1. Define Scope and Regulatory Compliance:

   • Map the target business processes (e.g., KYC, AML, Credit Risk) across all international jurisdictions.

   • Establish clear Service Level Objectives (SLOs) for scale, security, and cost reduction.

   • Define the AI Governance framework, including Explainable AI (XAI) requirements for all jurisdictions.

2. Establish IBM Data Unification Layer:

   • Implement data virtualization and metadata management to unify existing, siloed data sources (mainframes, distributed databases, cloud repositories) without moving them physically (ETL reduction).

   • Deploy IBM Guardium and watsonx.governance for unified, end-to-end data security and compliance monitoring.

3. Deploy PowerGraph Foundation:

   • Install and configure PowerGraph (KGNN) on the IBM Power systems, ensuring native access to mission-critical data for optimal speed and security.

   • Begin automated data ingestion to build the initial Knowledge Graph, linking entities (customers, accounts, transactions) across the unified data layer.

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Phase 2: AI Development and Operationalization

This phase focuses on building, testing, and governing the AI models that will drive the new system's intelligence and deploying them at enterprise scale.

1. Develop AI Models and GNNs:

   • Data science teams train advanced Graph Neural Network (GNN) models using data from the PowerGraph to detect complex, non-obvious fraud or risk patterns.

   • Develop Agentic AI models (e.g., for automated onboarding or auditing) to leverage the inferencing speed of the IBM Spyre Accelerator.

2. Deploy Plane Control (MLOps):

   • Implement Plane Control (Wallaroo.ai Option Plane) as the MLOps layer for the AI ecosystem. This platform handles model versioning, continuous integration/continuous deployment (CI/CD), and scaling across the international bank's footprint.

   • Configure low-latency inference endpoints on the IBM Power/Spyre systems to ensure real-time AI decisions directly within core banking workflows.

3. Integrate and Secure with Wingman:

   • Deploy Wingman to monitor the entire AI/ML system and the underlying Power infrastructure. This tool uses AI to detect anomalies in model behavior, data drift, and potential cyber threats in real time, guaranteeing system resilience.

   • Verify that the Plane Control and Wingman logs feed into the centralized governance and security platforms for continuous audit and compliance.

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Phase 3: Pilot, Rollout, and Optimization

The final phase involves rigorous testing, phased deployment, and establishing continuous improvement loops.

1. Pilot Deployment and Validation:

   • Run the new AI-driven system (PowerGraph, Plane Control, Wingman) alongside the legacy system in a single, well-controlled international branch or business line.

   • Measure the realized impact on KPIs: Security improvement (threat detection rate), Scale (transaction throughput), and Cost reduction (ETL time, manual auditing hours).

2. Phased International Rollout:

   • Execute a phased rollout across the remaining business units and international regions, adapting the AI models and governance rules to local regulatory requirements.

   • Use Wingman for continuous monitoring during the rollout to preemptively address security or performance issues.

3. Continuous Optimization:

   • Utilize Plane Control for automatic retraining and deployment of enhanced AI models based on new data and operational feedback.

   • The PowerGraph continues to expand its knowledge base, driving iterative improvements in the accuracy of KYC, AML, and credit risk models.

The video below discusses IBM's strategies for managing AI and data security within enterprise systems, which is foundational to this upgrade plan.

IBM Introduces Industry-first Software to Unify Agentic Governance and Security