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IBM Safer Payments v6.9: Modeling
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),...
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Duration
3 days
Course Code
6A930G
Available Formats
Classroom
Overview
Course Description
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.
Skills Gained
- Mandator structure
- Modeling Concepts
- Profiling Concept
- Rulesets and Rules
- Simulation Environment
- Analysis
- Model Components
- Python callouts
Who Can Benefit
- 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
Prerequisites
- Must be familiar with Unix command line navigation and configuration actions
- Some familiarity with statistical models
- Knowledge in Fraud Prevention for cashless payments
Course Details
Course Details
Day 1: Modeling approaches and Profiling
- 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: Rules and Simulation
- Introduction to Simulation workflow
- Sampling Techniques
- Rule Analyses and Rule Performance
- Rule Performance and Rule Scoring
Day 3: Model Factory and External Models
- Internal modeling capabilities (Rule Generator and Random Forest)
- Exporting and importing data for external modeling
- Python callouts
- PMML Model Import
- Point of Compromise
Schedule
FAQ
Does the course schedule include a Lunchbreak?
Classes typically include a 1-hour lunch break around midday. However, the exact break times and duration can vary depending on the specific class. Your instructor will provide detailed information at the start of the course.
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Do you provide hands-on training and exercises in an actual lab environment?
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Reviews
Both course material and instructor demonstrated a sound foundation on Maximo material
Good training material and good instruction. More time needs to be provided for the lab work.
Very good couse and again we would like to see more videos on removing FRUs
the interface was super easy to use and the instructions to get ready for the course was also very simple and easy to understand.
Course was great and the instructor had an answer for anything that was asked during the course.
