Abstract
Most industries are susceptible to fraud, which poses a risk to both businesses and consumers. According to The National Health Care Anti-Fraud Association, health care fraud alone causes the nation around $68 billion annually. This statistic does not include the numerous other industries where fraudulent activities occur daily. In addition, the growing amount of data that enterprises own makes it difficult for them to detect fraud. Businesses can benefit by using an analytical platform to fully integrate their data with artificial intelligence (AI) technology.
With IBM Cloud Pak® for Data on IBM Z, enterprises can modernize their data infrastructure, develop, and deploy machine learning (ML) and AI models, and instantiate highly efficient analytics deployment on IBM LinuxONE. Enterprises can create cutting-edge, intelligent, and interactive applications with embedded AI, colocate data with commercial applications, and use AI to make inferences.
This IBM Redguide publication presents a high-level overview of IBM Z. It describes IBM Cloud Pak for Data (CP4D) on IBM Z and IBM LinuxONE, the different features that are supported on the platform, and how the associated features can help enterprise customers in building AI and ML models by using core transactional data, which results in decreased latency and increased throughput.
This publication highlights real-time CP4D on IBM Z use cases. Real-time Clearing and Settlement Transactions, Trustworthy AI and its Role in Day-To-Day Monitoring, and the Prevention of Retail Crimes are use cases that are described in this publication. Using CP4D on IBM Z and LinuxONE, this publication shows how businesses can implement a highly efficient analytics deployment that minimizes latency, cost inefficiencies, and potential security exposures that are connected with data transportation.
Table of Contents
IBM Z: An overview
What is Cloud Pak for Data on IBM Z
Cloud Pak for Data capabilities on IBM Z and IBM LinuxONE
Open-source ecosystem
Why AI on IBM Z
AI use cases
Use case 1: Responsible AI augmented with risk and regulatory compliances
Use case 2: Credit default risk assessment
Use case 3: Clearing and settlement
Use case 4: Remaining Useful Life of an aircraft engine
Use case 5: AI-powered video analytics on an infant's motions for health prediction
Additional resources
Summary