Equitus KGNN, Wallaroo.ai, and IBM Power 11's MMA architecture, your proposed pilot project can create significant value for Dillard's department store, particularly for FCU (fraud control unit) and address correlation.
Value Proposition
The synergy between these three technologies can deliver value in the following ways:
Accelerated Fraud Detection: Equitus KGNN excels at transforming fragmented and disconnected data—such as customer transactions, purchase history, and address information—into a structured knowledge graph. This graph makes it easier to uncover hidden connections and patterns that are indicative of fraudulent activity.
High-Performance AI Inference: Wallaroo.ai's platform can then be used to rapidly deploy and manage AI models on the knowledge graph data for fraud detection and address correlation. Its optimization for IBM Power servers means these models can perform with high efficiency and low latency, essential for real-time applications.
On-Chip AI Acceleration: The IBM Power 11's Matrix Math Assist (MMA) architecture is designed to run AI inference workloads directly on the CPU, alongside Dillard's mission-critical transactional systems. This eliminates the need for separate, expensive GPU servers, reducing infrastructure costs and complexity. It also allows the AI models to process data where it resides, leading to a faster, more integrated solution for identifying fraudulent transactions and correlating addresses in real-time.
This combined approach provides a powerful, cost-effective, and highly performant solution for a major retail enterprise like Dillard's, enabling them to make smarter, faster decisions regarding fraud and customer data.
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