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The diagram path now follows a documented standard procedure (lookup the closest Oracle Architecture Center reference → confirm components → author absolute_layout → spec validator → render → visually verify) and ships persistent guardrails so layout regressions can't recur. Persistent procedure changes (apply to all users, all sessions): - tools/diagram_spec_validator.py — geometry checks (CONTAINER_TOO_THIN, CONTAINER_PADDING_VIOLATION, LABEL_OVERFLOW_PARENT) run BEFORE either renderer (drawio + PPTX). Catches the subnet-collapse / label-overflow bugs that the post-render drawio validator missed. - tools/oci_diagram_gen.py + tools/oci_pptx_diagram_gen.py — call the spec validator before emitting any output. Adds mysql / mysql_heatwave type aliases. - tools/archcenter_pattern_lookup.py — scores against cached page descriptions (not just the 1-line summary), supports --queries for multi-fragment composition, and applies synonym expansion via kb/architecture-center/synonyms.yaml so "LB HA cross AD" matches "load balancer high availability availability domain". - kb/architecture-center/synonyms.yaml — canonical synonym table (load balancer, autonomous database, data guard, …) used by the lookup scorer. KB enrichment: - tools/archcenter_description_fetcher.py + 121 cached _description.md under kb/diagram/assets/archcenter-refs/<slug>/. Removes the runtime dependency on docs.oracle.com when authoring specs and feeds the pattern-lookup scorer. - 110+ cached .drawio / .svg / .png references for offline reuse, plus the OCI Toolkit v24.2 import (kb/diagram/assets/oci-toolkit-drawio). Documentation: - docs/skill/output-formats.md — new "Standard diagram-generation procedure (MANDATORY)" + geometry rules + the new validator entry. - SKILL.md option 2 — references the mandatory procedure. - README.md — describes the spec validator, archcenter_pattern_lookup and description fetcher, and updates the KB-health table. Tooling that backs the procedure (cumulative across recent sessions): tools/archcenter_case_runner.py, archcenter_batch_driver.py, archcenter_zip_downloader.py, drawio_visual_validator.py, drawio_fidelity_eval.py, harvest_drawio_icon.py, import_oci_library.py, oci_pptx_diagram_gen.py, oci_pptx_render.py, refresh_pptx_icon_index.py. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
6.5 KiB
Stream fraud detection with NVIDIA Morpheus on Oracle Compute Cloud@Customer
- Source: https://docs.oracle.com/en/solutions/fraud-detection-nvidia-morpheus-compute-cloud/index.html
- Date: 2025-08
- Type: built-deployed
- Services: compute
- Tags: ai-ml, security
Summary (catalog)
Real-time fraud detection with NVIDIA Morpheus on Compute Cloud@Customer. GPU-accelerated ML pipeline for transaction monitoring. On-premises deployment for data sovereignty requirements.
Architecture (fetched from source)
Learn About Streaming Fraud Detection
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Learn About Streaming Fraud Detection
Oracle Compute Cloud@Customer , a key component of the Oracle Roving Edge Infrastructure portfolio, provides organizations a scalable solution to process sensitive data securely, with low latency—close to its source.
In this solution playbook, you learn how to use the NVIDIA Morpheus cybersecurity framework to deploy real-time, AI-driven fraud detection using a GPU-accelerated Morpheus pipeline, and publish the results using Compute Cloud@Customer . The solution enables instant fraud detection without waiting for batch jobs and keeps your data secure by locally processing it at the edge.
Before You Begin
Ensure you perform the deployment on a host machine with the following environment settings:
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Operating System : Ubuntu 24.04 LTS
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Platform : NVIDIA AI Enterprise on a single-node Oracle Compute Cloud@Customer instance equipped with an NVIDIA L40S GPU.
Before you begin, ensure the following tools are installed on the host machine:
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Docker and Docker Compose ( docker compose V2 )
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Git and Git LFS ( git-lfs )
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Python 3 and Pip
Workflow
The workflow leverages the NVIDIA Morpheus cybersecurity framework to perform GPU-accelerated inference on a stream of financial transaction data.
The following diagram shows a workflow through three swimlanes; Host environment, Apache Kafka , and the Morpheus pipeline:
Description of the illustration ai-driven-fraud-detection-workflow-arch.png
The pipeline ingests live transaction data through Apache Kafka , performs graph-based contextual analysis using a pre-trained Graph Sample and Aggregate ( GraphSAGE ) model, and executes final fraud classification using XGBoost . All stages are accelerated using NVIDIA RAPIDS libraries (cuDF, cuML), making the entire workflow GPU-optimized for high throughput. The following is how the process flows:
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Transaction data (.csv) is produced by a Python producer.
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Data goes into Kafka topic INPUT stream.
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Data is processed through the Morpheus pipeline in steps: reading from Kafka source, deserializing, constructing a graph, interfacing with Graph Neural Network (GNN) interface ( GraphSAGE ), classifying with XGBoost , serializing results, and writing to Kafka sink.
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Results are sent to Kafka topic OUTPUT stream.
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A Python consumer receives output and provides live fraud prediction.
Model Provenance
The inference pipeline at the core of this architecture uses two pre-trained machine learning models: a GraphSAGE GNN and an XGBoost classifier. These models were generated using a separate training pipeline, which is included in the Morpheus repository for reference.
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Training Script Location: examples/gnn_fraud_detection_pipeline/training.py .
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Process: The script processes a labeled historical dataset to train the GNN on graph-based features and the XGBoost model on the resulting embeddings.
In this solution, you don't have to run the training script becausethe pre-trained models are already provided. Focus on deploying and running the real-time inference pipeline.
This architecture supports the following components:
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Oracle Compute Cloud@Customer Oracle Compute Cloud@Customer is fully-managed, rack-scale infrastructure that lets you use OCI Compute anywhere. Gain the benefits of cloud automation and economics in your data center by running OCI Compute and GPU shapes with storage and networking services on Compute Cloud@Customer . You can run applications and harness the power of GenAI on cloud infrastructure in your data center while helping address data residency, security, and low-latency connections to local resources and real-time operations.
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RAPIDS cuDF/cuML RAPIDS cuDF/cuML are a suite of GPU-accelerated libraries for high-performance data manipulation and machine learning utilities within the Morpheus pipeline.
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Docker + Conda Docker + Conda provide a layered approach to dependency management, using Docker for OS-level isolation and Conda for managing the complex Python environment inside the container.
Considerations for Production
When implementing this solution in a production environment, consider the scalability and resilience that Kubernetes can provide. You can migrate this solution to OCI Kubernetes Engine ( OKE ) by:
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Containerizing the producer and consumer helper scripts.
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Deploying Kafka using a production-grade Kubernetes operator.
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Deploying the Morpheus pipeline as a Kubernetes job or deployment.
About Required Services and Roles
This solution requires the following services and roles:
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Oracle Compute Cloud@Customer
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NVIDIA AI Enterprise 6.0
Ubuntu Linux (or a compatible Linux distribution)
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Docker
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NVIDIA Morpheus 25.02
These are the roles needed for each service.
Service Name: Role Required to...
Oracle Compute Cloud@Customer : administrator Configure and deploy the NVIDIA AI Enterprise virtual machine instance, manage network resources, and ensure access to the NVIDIA L40S GPU.
Ubuntu Linux: root or user with sudo privileges
Install prerequisite software ( Docker , Git), manage system services, and execute Docker commands.
NVIDIA AI Enterprise : Account User
Pull the required NVIDIA Morpheus container image from the NVIDIA GPU Cloud ( NGC ) catalog.
See Oracle Products, Solutions, and Services to get what you need.
Title and Copyright Information
Fraud detection with NVIDIA Morpheus on Compute Cloud@Customer and Private Cloud Appliance
G38649-02
August 2025
Copyright © 2025,
Oracle and/or its affiliates.