Model Integration Tool

Integrate, audit and deploy custom models and scorecards

Rapidly integrate credit and risk models to get to market faster

Optimized, performance-driven business models and rules are only effective if you can validate and release them into production in a timely manner. Yet, organizations often struggle with the time, resources and money it takes going back and forth with IT to implement and deploy their models.

Model Integration Tool, an InterConnect® cloud-based decision tool, can help. It preserves precious resources while also accelerating speed-to-market, by empowering you—the business user—to rapidly integrate, audit and deploy custom models into production without reliance on IT resources.

In fact, the solution can help you potentially shave weeks off the model development lifecycle. It eliminates manual steps to bridge the gap from model development to production. Model Integration Tool gives you and other business users within your risk department the control to quickly deploy credit and risk models into production with built-in technology that automates the process.

With Model Integration Tool, you can:

  • Quickly deploy credit and risk models into production with streamlined technology that automates the process resulting in faster time to value of new models
  • Easily leverage models and scorecards across multiple environments for increased efficiencies
  • Minimize translation errors by eliminating the need for recoding models, which helps ensure greater consistency and drastically expedites the audit process
  • Use as a standalone tool that integrates into an existing system, or pair it with other InterConnect tools for an end-to-end decision management solution

It further optimizes internal efficiency and performance by eliminating the re-coding of new models for production with a unique ability to import IBM Statistical Package for the Social Sciences (SPSS), Statistical Analysis System (SAS), and R programming language trained models.

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Grow your business with automated credit and risk decisioning, fueled by diverse data