Principals

Overview

IBM Safer Payments is an innovative real-time payment fraud prevention and detection solution for all cashless payment types. IBM Safer Payments provides not only model capabilities based on inbuilt tools, but also the option to import externally built fraud models for real-time decisioning.

In this course, all of the IBM Safer Payments model capabilities are presented in detail. The following modelling concepts are covered: index, profiling techniques (with and without index sequence), model components comprised of rulesets, PMML, Python and Internal Random Forest, elements of the simulation environment including Rule Generation and Internal Random Forest, as well as the sampling techniques. All these concepts will be followed by the hands-on exercises.

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What You'll Learn

The objective of this course is to teach students the following topics:

  • Mandator structure
  • Modeling concepts
  • Profiling concept
  • Rulesets and Rules
  • Simulation environment
  • Analysis
  • Model components
  • Python callouts

Who Should Attend

  • External: Fraud Analysts, Application and System Admins managing Safer Payments (optional)
  • Internal: IBM Lab Services, IBM Support, IBM Technical Pre-Sales and IBM Business Partners
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Prerequisites

  • Must be familiar with Unix command line navigation and configuration actions
  • Some familiarity with statistical models
  • Knowledge in Fraud Prevention for cashless payments 

Learning Journey

Coming Soon...

Day 1: 

  • Safer Payments Data Dictionary
  • Modeling Approach (Internal & External Modeling)
  • Examine Indexes with and without sequences
  • Profiling in Safer Payments using index with sequence (Counter, Precedents, Pattern)
  • Profiling in Safer Payments using index without sequence (Calendar, Events, Device Identification, Formulas)
  • Introduction to Rules

Day 2:

  • Introduction to Simulation workflow
  • Sampling Techniques
  • Rule Analyses and Rule Performance
  • Rule Performance and Rule Scoring

Day 3:

  • Internal modeling capabilities (Rule Generator and Random Forest)
  • Exporting and importing data for external modeling
  • Python callouts
  • PMML Model Import
  • Point of Compromise

Instructors

Scott Duffy

Bestselling Azure & TOGAF® Trainer, Microsoft Azure MVP

4.8 (4.1k)
|
10 Courses
profile-instructor-a.png
Scott Duffy

Bestselling Azure & TOGAF® Trainer, Microsoft Azure MVP

4.8 (4.1k)
|
10 Courses
profile-instructor-a.png

Frequently Asked Questions (FAQs)

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    IBM certifications validate your skills and expertise in a wide range of technologies, including hybrid cloud, data & AI, security, IT infrastructure, and business applications.

    These certifications are globally recognized and can help you advance your career, increase your earning potential, and demonstrate your commitment to professional development.

    IBM-certified professionals are in high demand, making them valuable assets to any organization seeking to leverage IBM's innovative solutions.

  • What to expect for the examination?

    IBM offers a variety of certification exams at different levels (Basic, Intermediate, and Advanced) covering various technologies and job roles.

    Exams typically consist of multiple-choice questions and may include scenario-based questions that assess your ability to apply your knowledge in real-world situations.

    Note: Certification requirements and policies may be updated by IBM from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.

  • How long is IBM certification valid for?

    The validity period of IBM certifications varies. Some certifications, particularly those focused on specific product versions or technologies, may have expiration dates.

    However, many IBM certifications, especially those focused on broader skills or newer technologies, do not have an expiration date.

    Note: Certification requirements and policies may be updated by IBM from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.

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