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 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 that students are expected to complete.

Refer to course overview.

IBM Safer Payments users (Fraud Analysts, Fraud Investigators and optional: System Administrators), IBM Lab experts, and IBM Business Partners

• Business Knowledge
• Some Familiarity with statistical models
• Understanding Safer Payments Data Inputs concepts

• Mandator Structure and its elements
• Sandbox Environment
• Modeling Concepts in Safer Payments
• Index for Profiling
• Profiling based on index with sequence
• Profiling based on index without sequence
• Profiling using Formula
• Ruleset, Rule Creation, and Rule Action
• Simulation Workflow
• Simulation: Data Selection and Sampling techniques
• Simulation: Attribute usage
• Simulation: Queries
• Simulation: Rule Analysis
• Simulation: Rule Performance
• Simulation: Rule Scoring
• Simulation: Rule optimization
• Inbuild Model Components: Rule Generation
• Inbuild Model Components: Random Forest
• Supported external Model Components: PMML
• Supported external Model Components: Python
• Collusion Algorithm