title
Please take a moment to fill out this form. We will get back to you as soon as possible.
All fields marked with an asterisk (*) are mandatory.
IBM Safer Payments 6.8: Modeling
Course Description
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 details. The following modelling concepts are covered: index, profiling techniques (with aIBM Safer Payments is an enterprise fraud detection solution. It is a designed and focused on real time payment transaction processing. Safer Payments provides complex and dynamic rules building, evaluation and execution. Built with real time performance, management, and redundancy in mind.nd 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 that students are expected to execute.
Objectives
Please refer to course overview.
Audience
Fraud Analysts, Application and System Administrators), IBM Lab Services, IBM Support, 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
Topics
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
Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
Course Added To Shopping Cart
bla
bla
bla
bla
bla
bla
Self-Paced Training Terms & Conditions
Exam Terms & Conditions
Sorry, there are no classes that meet your criteria.
Please contact us to schedule a class.
STOP! Before You Leave
Save 0% on this course!
Take advantage of our online-only offer & save 0% on any course !
Promo Code skip0 will be applied to your registration
Purchase Information
title
Please take a moment to fill out this form. We will get back to you as soon as possible.
All fields marked with an asterisk (*) are mandatory.