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Real Time Fraud Detection Analytics On Ibm System Z
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Book Synopsis Real-time Fraud Detection Analytics on IBM System z by : Mike Ebbers
Download or read book Real-time Fraud Detection Analytics on IBM System z written by Mike Ebbers and published by IBM Redbooks. This book was released on 2013-04-11 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: Payment fraud can be defined as an intentional deception or misrepresentation that is designed to result in an unauthorized benefit. Fraud schemes are becoming more complex and difficult to identify. It is estimated that industries lose nearly $1 trillion USD annually because of fraud. The ideal solution is where you avoid making fraudulent payments without slowing down legitimate payments. This solution requires that you adopt a comprehensive fraud business architecture that applies predictive analytics. This IBM® Redbooks® publication begins with the business process flows of several industries, such as banking, property/casualty insurance, and tax revenue, where payment fraud is a significant problem. This book then shows how to incorporate technological advancements that help you move from a post-payment to pre-payment fraud detection architecture. Subsequent chapters describe a solution that is specific to the banking industry that can be easily extrapolated to other industries. This book describes the benefits of doing fraud detection on IBM System z®. This book is intended for financial decisionmakers, consultants, and architects, in addition to IT administrators.
Book Synopsis Enabling Real-time Analytics on IBM z Systems Platform by : Lydia Parziale
Download or read book Enabling Real-time Analytics on IBM z Systems Platform written by Lydia Parziale and published by IBM Redbooks. This book was released on 2016-08-08 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regarding online transaction processing (OLTP) workloads, IBM® z SystemsTM platform, with IBM DB2®, data sharing, Workload Manager (WLM), geoplex, and other high-end features, is the widely acknowledged leader. Most customers now integrate business analytics with OLTP by running, for example, scoring functions from transactional context for real-time analytics or by applying machine-learning algorithms on enterprise data that is kept on the mainframe. As a result, IBM adds investment so clients can keep the complete lifecycle for data analysis, modeling, and scoring on z Systems control in a cost-efficient way, keeping the qualities of services in availability, security, reliability that z Systems solutions offer. Because of the changed architecture and tighter integration, IBM has shown, in a customer proof-of-concept, that a particular client was able to achieve an orders-of-magnitude improvement in performance, allowing that client's data scientist to investigate the data in a more interactive process. Open technologies, such as Predictive Model Markup Language (PMML) can help customers update single components instead of being forced to replace everything at once. As a result, you have the possibility to combine your preferred tool for model generation (such as SAS Enterprise Miner or IBM SPSS® Modeler) with a different technology for model scoring (such as Zementis, a company focused on PMML scoring). IBM SPSS Modeler is a leading data mining workbench that can apply various algorithms in data preparation, cleansing, statistics, visualization, machine learning, and predictive analytics. It has over 20 years of experience and continued development, and is integrated with z Systems. With IBM DB2 Analytics Accelerator 5.1 and SPSS Modeler 17.1, the possibility exists to do the complete predictive model creation including data transformation within DB2 Analytics Accelerator. So, instead of moving the data to a distributed environment, algorithms can be pushed to the data, using cost-efficient DB2 Accelerator for the required resource-intensive operations. This IBM Redbooks® publication explains the overall z Systems architecture, how the components can be installed and customized, how the new IBM DB2 Analytics Accelerator loader can help efficient data loading for z Systems data and external data, how in-database transformation, in-database modeling, and in-transactional real-time scoring can be used, and what other related technologies are available. This book is intended for technical specialists and architects, and data scientists who want to use the technology on the z Systems platform. Most of the technologies described in this book require IBM DB2 for z/OS®. For acceleration of the data investigation, data transformation, and data modeling process, DB2 Analytics Accelerator is required. Most value can be achieved if most of the data already resides on z Systems platforms, although adding external data (like from social sources) poses no problem at all.
Book Synopsis Optimized Inferencing and Integration with AI on IBM zSystems: Introduction, Methodology, and Use Cases by : Makenzie Manna
Download or read book Optimized Inferencing and Integration with AI on IBM zSystems: Introduction, Methodology, and Use Cases written by Makenzie Manna and published by IBM Redbooks. This book was released on 2022-11-30 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: In today's fast-paced, ever-growing digital world, you face various new and complex business problems. To help resolve these problems, enterprises are embedding artificial intelligence (AI) into their mission-critical business processes and applications to help improve operations, optimize performance, personalize the user experience, and differentiate themselves from the competition. Furthermore, the use of AI on the IBM® zSystems platform, where your mission-critical transactions, data, and applications are installed, is a key aspect of modernizing business-critical applications while maintaining strict service-level agreements (SLAs) and security requirements. This colocation of data and AI empowers your enterprise to optimally and easily deploy and infuse AI capabilities into your enterprise workloads with the most recent and relevant data available in real time, which enables a more transparent, accurate, and dependable AI experience. This IBM Redpaper publication introduces and explains AI technologies and hardware optimizations, and demonstrates how to leverage certain capabilities and components to enable AI solutions in business-critical use cases, such as fraud detection and credit risk scoring, on the platform. Real-time inferencing with AI models, a capability that is critical to certain industries and use cases, now can be implemented with optimized performance thanks to innovations like IBM zSystems Integrated Accelerator for AI embedded in the Telum chip within IBM z16TM. This publication describes and demonstrates the implementation and integration of the two end-to-end solutions (fraud detection and credit risk), from developing and training the AI models to deploying the models in an IBM z/OS® V2R5 environment on IBM z16 hardware, and integrating AI functions into an application, for example an IBM z/OS Customer Information Control System (IBM CICS®) application. We describe performance optimization recommendations and considerations when leveraging AI technology on the IBM zSystems platform, including optimizations for micro-batching in IBM Watson® Machine Learning for z/OS. The benefits that are derived from the solutions also are described in detail, including how the open-source AI framework portability of the IBM zSystems platform enables model development and training to be done anywhere, including on IBM zSystems, and enables easy integration to deploy on IBM zSystems for optimal inferencing. Thus, allowing enterprises to uncover insights at the transaction-level while taking advantage of the speed, depth, and securability of the platform. This publication is intended for technical specialists, site reliability engineers, architects, system programmers, and systems engineers. Technologies that are covered include TensorFlow Serving, WMLz, IBM Cloud Pak® for Data (CP4D), IBM z/OS Container Extensions (zCX), IBM CICS, Open Neural Network Exchange (ONNX), and IBM Deep Learning Compiler (zDLC).
Book Synopsis Apache Spark Implementation on IBM z/OS by : Lydia Parziale
Download or read book Apache Spark Implementation on IBM z/OS written by Lydia Parziale and published by IBM Redbooks. This book was released on 2016-08-13 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: The term big data refers to extremely large sets of data that are analyzed to reveal insights, such as patterns, trends, and associations. The algorithms that analyze this data to provide these insights must extract value from a wide range of data sources, including business data and live, streaming, social media data. However, the real value of these insights comes from their timeliness. Rapid delivery of insights enables anyone (not only data scientists) to make effective decisions, applying deep intelligence to every enterprise application. Apache Spark is an integrated analytics framework and runtime to accelerate and simplify algorithm development, depoyment, and realization of business insight from analytics. Apache Spark on IBM® z/OS® puts the open source engine, augmented with unique differentiated features, built specifically for data science, where big data resides. This IBM Redbooks® publication describes the installation and configuration of IBM z/OS Platform for Apache Spark for field teams and clients. Additionally, it includes examples of business analytics scenarios.
Book Synopsis Commercial Data Mining by : David Nettleton
Download or read book Commercial Data Mining written by David Nettleton and published by Elsevier. This book was released on 2014-01-29 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial Data Mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling. Commercial Data Mining includes case studies and practical examples from Nettleton's more than 20 years of commercial experience. Real-world cases covering customer loyalty, cross-selling, and audience prediction in industries including insurance, banking, and media illustrate the concepts and techniques explained throughout the book. Illustrates cost-benefit evaluation of potential projects Includes vendor-agnostic advice on what to look for in off-the-shelf solutions as well as tips on building your own data mining tools Approachable reference can be read from cover to cover by readers of all experience levels Includes practical examples and case studies as well as actionable business insights from author's own experience
Book Synopsis Accelerating Data Transformation with IBM DB2 Analytics Accelerator for z/OS by : Ute Baumbach
Download or read book Accelerating Data Transformation with IBM DB2 Analytics Accelerator for z/OS written by Ute Baumbach and published by IBM Redbooks. This book was released on 2015-12-11 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transforming data from operational data models to purpose-oriented data structures has been commonplace for the last decades. Data transformations are heavily used in all types of industries to provide information to various users at different levels. Depending on individual needs, the transformed data is stored in various different systems. Sending operational data to other systems for further processing is then required, and introduces much complexity to an existing information technology (IT) infrastructure. Although maintenance of additional hardware and software is one component, potential inconsistencies and individually managed refresh cycles are others. For decades, there was no simple and efficient way to perform data transformations on the source system of operational data. With IBM® DB2® Analytics Accelerator, DB2 for z/OS is now in a unique position to complete these transformations in an efficient and well-performing way. DB2 for z/OS completes these while connecting to the same platform as for operational transactions, helping you to minimize your efforts to manage existing IT infrastructure. Real-time analytics on incoming operational transactions is another demand. Creating a comprehensive scoring model to detect specific patterns inside your data can easily require multiple iterations and multiple hours to complete. By enabling a first set of analytical functionality in DB2 Analytics Accelerator, those dedicated mining algorithms can now be run on an accelerator to efficiently perform these modeling tasks. Given the speed of query processing on an accelerator, these modeling tasks can now be performed much quicker compared to traditional relational database management systems. This speed enables you to keep your scoring algorithms more up-to-date, and ultimately adapt more quickly to constantly changing customer behaviors. This IBM Redbooks® publication describes the new table type that is introduced with DB2 Analytics Accelerator V4.1 PTF5 that enables more efficient data transformations. These tables are called accelerator-only tables, and can exist on an accelerator only. The tables benefit from the accelerator performance characteristics, while maintaining access through existing DB2 for z/OS application programming interfaces (APIs). Additionally, we describe the newly introduced analytical capabilities with DB2 Analytics Accelerator V5.1, putting you in the position to efficiently perform data modeling for online analytical requirements in your DB2 for z/OS environment. This book is intended for technical decision-makers who want to get a broad understanding about the analytical capabilities and accelerator-only tables of DB2 Analytics Accelerator. In addition, you learn about how these capabilities can be used to accelerate in-database transformations and in-database analytics in various environments and scenarios, including the following scenarios: Multi-step processing and reporting in IBM DB2 Query Management FacilityTM, IBM Campaign, or Microstrategy environments In-database transformations using IBM InfoSphere® DataStage® Ad hoc data analysis for data scientists In-database analytics using IBM SPSS® Modeler
Book Synopsis Reliability and Performance with IBM DB2 Analytics Accelerator V4.1 by : Paolo Bruni
Download or read book Reliability and Performance with IBM DB2 Analytics Accelerator V4.1 written by Paolo Bruni and published by IBM Redbooks. This book was released on 2015-05-11 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: The IBM® DB2® Analytics Accelerator for IBM z/OS® is a high-performance appliance that integrates the IBM zEnterprise® infrastructure with IBM PureDataTM for Analytics, powered by IBM Netezza® technology. With this integration, you can accelerate data-intensive and complex queries in a DB2 for z/OS highly secure and available environment. DB2 and the Analytics Accelerator appliance form a self-managing hybrid environment running online transaction processing and online transactional analytical processing concurrently and efficiently. These online transactions run together with business intelligence and online analytic processing workloads. DB2 Analytics Accelerator V4.1 expands the value of high-performance analytics. DB2 Analytics Accelerator V4.1 opens to static Structured Query Language (SQL) applications and row set processing, minimizes data movement, reduces latency, and improves availability. This IBM Redbooks® publication provides technical decision-makers with an understanding of the benefits of version 4.1 of the Analytics Accelerator with DB2 11 for z/OS. It describes the installation of the new functions, and the advantages to existing analytical processes as measured in our test environment. This book also introduces the DB2 Analytics Accelerator Loader V1.1, a tool that facilitates the data population of the DB2 Analytics Accelerator.
Book Synopsis IBM z13 Technical Guide by : Octavian Lascu
Download or read book IBM z13 Technical Guide written by Octavian Lascu and published by IBM Redbooks. This book was released on 2016-11-11 with total page 610 pages. Available in PDF, EPUB and Kindle. Book excerpt: Digital business has been driving the transformation of underlying IT infrastructure to be more efficient, secure, adaptive, and integrated. Information Technology (IT) must be able to handle the explosive growth of mobile clients and employees. IT also must be able to use enormous amounts of data to provide deep and real-time insights to help achieve the greatest business impact. This IBM® Redbooks® publication addresses the IBM Mainframe, the IBM z13TM. The IBM z13 is the trusted enterprise platform for integrating data, transactions, and insight. A data-centric infrastructure must always be available with a 99.999% or better availability, have flawless data integrity, and be secured from misuse. It needs to be an integrated infrastructure that can support new applications. It needs to have integrated capabilities that can provide new mobile capabilities with real-time analytics delivered by a secure cloud infrastructure. IBM z13 is designed with improved scalability, performance, security, resiliency, availability, and virtualization. The superscalar design allows the z13 to deliver a record level of capacity over the prior IBM z SystemsTM. In its maximum configuration, z13 is powered by up to 141 client characterizable microprocessors (cores) running at 5 GHz. This configuration can run more than 110,000 millions of instructions per second (MIPS) and up to 10 TB of client memory. The IBM z13 Model NE1 is estimated to provide up to 40% more total system capacity than the IBM zEnterprise® EC12 (zEC1) Model HA1. This book provides information about the IBM z13 and its functions, features, and associated software support. Greater detail is offered in areas relevant to technical planning. It is intended for systems engineers, consultants, planners, and anyone who wants to understand the IBM z Systems functions and plan for their usage. It is not intended as an introduction to mainframes. Readers are expected to be generally familiar with existing IBM z Systems technology and terminology.
Book Synopsis Hybrid Analytics Solution using IBM DB2 Analytics Accelerator for z/OS V3.1 by : Paolo Bruni
Download or read book Hybrid Analytics Solution using IBM DB2 Analytics Accelerator for z/OS V3.1 written by Paolo Bruni and published by IBM Redbooks. This book was released on 2013-09-27 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: The IBM® DB2® Analytics Accelerator Version 3.1 for IBM z/OS® (simply called Accelerator in this book) is a union of the IBM System z® quality of service and IBM Netezza® technology to accelerate complex queries in a DB2 for z/OS highly secure and available environment. Superior performance and scalability with rapid appliance deployment provide an ideal solution for complex analysis. In this IBM Redbooks® publication, we provide technical decision-makers with a broad understanding of the benefits of Version 3.1 of the Accelerator's major new functions. We describe their installation and the advantages to existing analytical processes as measured in our test environment. We also describe the IBM zEnterprise® Analytics System 9700, a hybrid System z solution offering that is surrounded by a complete set of optional packs to enable customers to custom tailor the system to their unique needs..
Book Synopsis IBM Cloud Pak for Data on IBM Z by : Jasmeet Bhatia
Download or read book IBM Cloud Pak for Data on IBM Z written by Jasmeet Bhatia and published by IBM Redbooks. This book was released on 2023-07-11 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis IBM z13 and IBM z13s Technical Introduction by : Bill White
Download or read book IBM z13 and IBM z13s Technical Introduction written by Bill White and published by IBM Redbooks. This book was released on 2017-01-10 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM® Redbooks® publication introduces the latest IBM z SystemsTM platforms, the IBM z13TM and IBM z13s. It includes information about the z Systems environment and how it can help integrate data, transactions, and insight for faster and more accurate business decisions. The z13 and z13s are state-of-the-art data and transaction systems that deliver advanced capabilities that are vital to modern IT infrastructures. These capabilities include: Accelerated data and transaction serving Integrated analytics Access to the API economy Agile development and operations Efficient, scalable, and secure cloud services End-to-end security for data and transactions This book explains how these systems use both new innovations and traditional z Systems strengths to satisfy growing demand for cloud, analytics, and mobile applications. With one of these z Systems platforms as the base, applications can run in a trusted, reliable, and secure environment that both improves operations and lessens business risk.
Book Synopsis Optimizing DB2 Queries with IBM DB2 Analytics Accelerator for z/OS by : Paolo Bruni
Download or read book Optimizing DB2 Queries with IBM DB2 Analytics Accelerator for z/OS written by Paolo Bruni and published by IBM Redbooks. This book was released on 2012-12-20 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: The IBM® DB2® Analytics Accelerator Version 2.1 for IBM z/OS® (also called DB2 Analytics Accelerator or Query Accelerator in this book and in DB2 for z/OS documentation) is a marriage of the IBM System z® Quality of Service and Netezza® technology to accelerate complex queries in a DB2 for z/OS highly secure and available environment. Superior performance and scalability with rapid appliance deployment provide an ideal solution for complex analysis. This IBM Redbooks® publication provides technical decision-makers with a broad understanding of the IBM DB2 Analytics Accelerator architecture and its exploitation by documenting the steps for the installation of this solution in an existing DB2 10 for z/OS environment. In this book we define a business analytics scenario, evaluate the potential benefits of the DB2 Analytics Accelerator appliance, describe the installation and integration steps with the DB2 environment, evaluate performance, and show the advantages to existing business intelligence processes.
Book Synopsis Using IBM z/OS WLM to Measure Mobile and Other Workloads by : IBM Client Center Montpellier
Download or read book Using IBM z/OS WLM to Measure Mobile and Other Workloads written by IBM Client Center Montpellier and published by IBM Redbooks. This book was released on 2016-10-25 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM® RedpaperTM publication discusses the need to monitor and measure different workloads, especially mobile workloads. It introduces the workload classification capabilities of IBM z SystemsTM platforms and helps you to understand how recent enhancements to IBM MVSTM Workload Management (WLM) and other IBM software products can be used to measure the processor cost of mobile workloads. This paper looks at how mobile-initiated and other transactions in IBM CICS®, IMSTM, DB2®, and WebSphere® Application Server can be "tagged and tracked" using WLM. For each of these subsystems, the options for classifying mobile requests and using WLM to measure mobile workloads are reviewed. A scenario is considered in which a bank is witnessing a significant growth in mobile initiated transactions, and wants to monitor and measure the mobile channels more closely. This paper outlines how the bank can use WLM to do this. This publication can help you to configure WLM mobile classification rules. It can also help you to interpret Workload Activity reports from IBM RMFTM Post Processor and to report on the CPU consumption of different workloads, including mobile and public cloud workloads.
Book Synopsis Systems of Insight for Digital Transformation: Using IBM Operational Decision Manager Advanced and Predictive Analytics by : Whei-Jen Chen
Download or read book Systems of Insight for Digital Transformation: Using IBM Operational Decision Manager Advanced and Predictive Analytics written by Whei-Jen Chen and published by IBM Redbooks. This book was released on 2015-12-03 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Systems of record (SORs) are engines that generates value for your business. Systems of engagement (SOE) are always evolving and generating new customer-centric experiences and new opportunities to capitalize on the value in the systems of record. The highest value is gained when systems of record and systems of engagement are brought together to deliver insight. Systems of insight (SOI) monitor and analyze what is going on with various behaviors in the systems of engagement and information being stored or transacted in the systems of record. SOIs seek new opportunities, risks, and operational behavior that needs to be reported or have action taken to optimize business outcomes. Systems of insight are at the core of the Digital Experience, which tries to derive insights from the enormous amount of data generated by automated processes and customer interactions. Systems of Insight can also provide the ability to apply analytics and rules to real-time data as it flows within, throughout, and beyond the enterprise (applications, databases, mobile, social, Internet of Things) to gain the wanted insight. Deriving this insight is a key step toward being able to make the best decisions and take the most appropriate actions. Examples of such actions are to improve the number of satisfied clients, identify clients at risk of leaving and incentivize them to stay loyal, identify patterns of risk or fraudulent behavior and take action to minimize it as early as possible, and detect patterns of behavior in operational systems and transportation that lead to failures, delays, and maintenance and take early action to minimize risks and costs. IBM® Operational Decision Manager is a decision management platform that provides capabilities that support both event-driven insight patterns, and business-rule-driven scenarios. It also can easily be used in combination with other IBM Analytics solutions, as the detailed examples will show. IBM Operational Decision Manager Advanced, along with complementary IBM software offerings that also provide capability for systems of insight, provides a way to deliver the greatest value to your customers and your business. IBM Operational Decision Manager Advanced brings together data from different sources to recognize meaningful trends and patterns. It empowers business users to define, manage, and automate repeatable operational decisions. As a result, organizations can create and shape customer-centric business moments. This IBM Redbooks® publication explains the key concepts of systems of insight and how to implement a system of insight solution with examples. It is intended for IT architects and professionals who are responsible for implementing a systems of insights solution requiring event-based context pattern detection and deterministic decision services to enhance other analytics solution components with IBM Operational Decision Manager Advanced.
Book Synopsis Machine Learning with Business Rules on IBM Z: Acting on Your Insights by : Mike Johnson
Download or read book Machine Learning with Business Rules on IBM Z: Acting on Your Insights written by Mike Johnson and published by IBM Redbooks. This book was released on 2019-12-11 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Redpaper introduces the integration between two IBM products that you might like to consider when implementing a modern agile solution on your Z systems. The document briefly introduces Operational Decision Manager on z/OS and Machine learning on z/OS. In the case of Machine Learning we focus on the aspect of real-time scoring models and how these can be used with Business Rules to give better decisions. Note: Important changes since this document was written: This document was written for an older release of Operational Decision Manager for z/OS (ODM for z/OS). ODM for z/OS 8.9.1 required the writing of custom Java code to access a Watson Machine Learning for z/OS Scoring Service (this can be seen in ). Since that time ODM for z/OS version 8.10.1 has been released and much improves the integration experience. Integrating the two products no longer requires custom Java code. Using ODM for z/OS 8.10.1 or later you can use an automated wizard in the ODM tooling to: Browse and select a model from Watson Machine Learning Import the Machine Learning data model into your rule project Automatically generate a template rule that integrates a call to the Watson Machine Learning scoring service Download and read this document for: Individual introductions to ODM for z/OS and Machine learning Discussions on the benefits of using the two technologies together Information on integrating if you have not yet updated to ODM for z/OS 8.10.1 For information about the machine learning integration in ODM for z/OS 8.10.1 see IBM Watson Machine Learning for z/OS integration topic in the ODM for z/OS 8.10.x Knowledge Center
Book Synopsis Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques by : Bart Baesens
Download or read book Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques written by Bart Baesens and published by John Wiley & Sons. This book was released on 2015-07-27 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: Detect fraud earlier to mitigate loss and prevent cascading damage Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This invaluable guide details both the theory and technical aspects of these techniques, and provides expert insight into streamlining implementation. Coverage includes data gathering, preprocessing, model building, and post-implementation, with comprehensive guidance on various learning techniques and the data types utilized by each. These techniques are effective for fraud detection across industry boundaries, including applications in insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and more, giving you a highly practical framework for fraud prevention. It is estimated that a typical organization loses about 5% of its revenue to fraud every year. More effective fraud detection is possible, and this book describes the various analytical techniques your organization must implement to put a stop to the revenue leak. Examine fraud patterns in historical data Utilize labeled, unlabeled, and networked data Detect fraud before the damage cascades Reduce losses, increase recovery, and tighten security The longer fraud is allowed to go on, the more harm it causes. It expands exponentially, sending ripples of damage throughout the organization, and becomes more and more complex to track, stop, and reverse. Fraud prevention relies on early and effective fraud detection, enabled by the techniques discussed here. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques helps you stop fraud in its tracks, and eliminate the opportunities for future occurrence.
Book Synopsis Modern Mainframe Development by : Tom Taulli
Download or read book Modern Mainframe Development written by Tom Taulli and published by "O'Reilly Media, Inc.". This book was released on 2022-03-16 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: Even as spending on digital transformation continues to skyrocket, mainframes nevertheless have major advantages for global enterprises. These systems still process huge amounts of information and allow for highly secure processing. In this practical book, author Tom Taulli shows software developers how to pursue a hybrid approach by integrating traditional mainframes and applications with modern digital systems. By the end of the book, you'll have a solid understanding of the mainframe architecture and ecosystem, including core concepts and technologies such as COBOL, JCL, DB2, VSAM, and CICS. You'll learn how to blend in newer technologies such as the cloud, AI and machine learning, and the use of microservices. This handbook is indispensable for enterprises looking to thrive in the new digital world. Learn strategies and approaches for mainframe DevOps Update and maintain existing mainframe code, and analyze and resolve common errors Apply modern approaches to the mainframe, including microservices, APIs, cloud, and AI and machine learning Work with datasets and databases and put together effective reports Understand how to work with modern cloud systems, like AWS, for pursuing data migration
