1 Overview of AI in Cloud Scale Monetization
Oracle Communications Cloud Scale Monetization provides artificial intelligence (AI) and machine learning (ML) services that help you optimize revenue management and billing operations. This document introduces the AI features available in Cloud Scale Monetization.
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About AI in Cloud Scale Monetization
Cloud Scale Monetization offers AI-based microservices that enable you to build end-to-end machine learning (ML) workflows. You use these workflows to create customized models and implement monetization and charging features tailored to your business needs.
The microservices are highly extensible and configurable. They deliver flexibility and scalability, and are certified for use with both Oracle Cloud Infrastructure (OCI) and other private cloud environment.
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Oracle recommends using OCI for a seamless experience, including OCI Data Flow, OCI Data Science, OCI Object Storage, and advanced model management capabilities provided by OCI Data Science.
You can integrate the AI microservices with Cloud Scale Monetization products such as Oracle Communications Billing and Revenue Management (BRM), Oracle Communications Elastic Charging Engine (ECE), Oracle Communications Billing Care, and Oracle Communications Convergent Charging Controller.
With AI functionality in Cloud Scale Monetization, you can:
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Create data sources to build efficient ML workflows. Collect and fetch data in bulk or as needed, and use this data to train models.
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Train models based on configured data sources. Select which features, such as user data, subscription data, usage data, metrics data, to include in model training.
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Use inference services to make predictions. Analyze current data and generate predictions for future scenarios. For example, based on a customer’s usage pattern, you can predict the offer a customer is most likely to choose.
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Detect anomalies in time-series operational metrics. Use trained models to analyze current data and identify unusual patterns or behavior.
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Use AI agents to answer supported product questions in natural language. For example, you can use the Billing Care AI Agent to retrieve and summarize supported billing information.
You can also use AI services with external data sources and AI agents. For example, you can collect data from BRM and Siebel, train models that return multiple related prediction outputs, and use an AI agent to request predictions through a Model Context Protocol (MCP) interface.
This capability supports a range of use cases, including, but not limited to, next best offer recommendations, in which the AI services use customer, usage, product, and offer data to recommend suitable charge and discount offers.
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The AI-based microservices framework and related capabilities are newly introduced and are currently offered as experimental functionality.