IBM Safer Payments Modeling Training (V6.6) (6A630G)


IBM 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.

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.


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


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


  • Mandator structure 
  • Modeling Concepts 
  • Profiling Concept
  • Rulesets and Rules
  • Simulation Environment 
  • Analysis
  • Model Components
  • Python callouts
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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