IBM Safer Payments 6.8: Modeling (6A830G)

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.

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

Objective

Please refer to course overview.

Geef details weer

Course Outline

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
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