"Power-Up On Prem"
Achieving Security, Cost and Energy Savings: Investing in Advanced Knowledge Graphing
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Proposal: Requesting $50 million investment for AIMLUX.ai is designed to achieve a profound reduction in operational hours and a step-change in compliance effectiveness by integrating next-generation AI directly into MUFG's secure core systems (IBM Power).
The key benefits—ETL reduction, auditing improvement (FCU), and better Know Your Customer (KYC)—are all enabled by using Equitus.us PowerGraph as a Knowledge Graph Neural Network (KGNN) to establish immediate, contextual relationships between data, bypassing manual data plumbing.
Direct Benefits of KGNN on IBM Power/Spyre
| Operational Area | Challenge with Traditional Systems | How PowerGraph (KGNN) on Power/Spyre Solves It | Reduction in Annual Workable Hours |
| ETL (Extract, Transform, Load) | Data must be manually structured, cleaned, and moved between siloed systems before it can be analyzed (the "data plumbing" bottleneck). | Auto ETL and Semantic Extraction: The KGNN automatically ingests disparate data (structured, unstructured) and maps entities/relationships into a unified graph. This eliminates the majority of manual pipeline work. | High: Reduction of hundreds of thousands of hours in data engineering and preparation across the enterprise. |
| Auditing & Financial Crime Unit (FCU) | Fraud detection and AML checks rely on simple, rule-based alerts applied to flat data, leading to high false-positive rates and labor-intensive manual case review. | Relationship-Based Risk: The KGNN models hidden relationships (e.g., a customer, their business, and a transacting counterparty) that traditional systems miss. Spyre accelerates the inferencing of these complex, deep-layer Graph Neural Networks for real-time risk scoring. | Moderate to High: Significant reduction in manual false-positive investigation and faster closure of audit cases due to traceable, explainable AI decisions. |
| Know Your Customer (KYC) | Onboarding is slow and costly, requiring analysts to manually review dozens of documents and cross-reference multiple systems to establish a full customer profile and risk rating. | Holistic Context & Agentic AI: The KGNN provides a 360-degree, real-time view of the customer, their network, and associated risks from a single query. Generative AI agents (powered by Spyre) use this context to auto-complete documentation, flag inconsistencies, and accelerate onboarding from weeks to minutes/hours. | High: Drastic cut in client onboarding time and analyst effort, improving customer experience and compliance speed. |
Before and After: Integration with Equitus PowerGraph (KGNN)
The diagrams illustrate the fundamental architectural shift from a siloed, batch-processed data environment to a secure, real-time, relationship-driven AI platform.
Before: Traditional Banking Data Architecture
In this model, data is copied, transformed, and moved repeatedly, increasing complexity, latency, and security exposure.
| Data Sources | ETL Bottleneck |
| Customer Databases (DB2, Oracle) | Manual ETL Processes |
| Transaction Logs (Mainframe) | (Batch processing, data cleansing, complex joins) |
| Compliance Documents (PDFs) | |
| Siloed Analytical Systems | |
| Legacy Fraud Detection | KYC/AML Systems |
| (Results in high latency, high false positives, and slow human auditing) |
After: Integrated AI Architecture with PowerGraph (KGNN)
The KGNN on IBM Power becomes the secure single source of truth for all relationship data, and the Spyre accelerator ensures AI runs instantly alongside mission-critical systems.
| Core Infrastructure: IBM Power 10/11 + Spyre Accelerator (High Security, Low Latency) |
| Wallaroo.ai Option Plane (MLOps/Deployment Layer) |
| Equitus.us PowerGraph (KGNN) |
| (Automatic Data Ingestion & Semantic Mapping) |
| AI-Powered Applications |
| Real-Time KYC/Onboarding (Instant Risk Scoring) |
| Advanced FCU/AML (Deep Network Analysis) |
| AI-AIX (Automated System Operations) |
| Direct Data Sources |
| Customer/Core Data |
Key takeaway: The KGNN eliminates the ETL bottleneck by auto-mapping relationships, allowing AI models accelerated by Spyre to make real-time decisions directly on the most secure, compliant hardware (IBM Power).
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