LENGTH: 24 Hours (3 days)
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 execute.
IBM Safer Payments users (Fraud Analysts, Fraud Investigators and optional: System Administrators), IBM Lab experts, and IBM Business Partners.
Mandator Structure and its elements
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/ Rule Action
Simulation: Data Selection and Sampling techniques
Simulation: Attribute usage
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