Automated Credit Risk strategies began to emerge in the 1970s and 1980s. Back then, decision trees were the standard automation approach. Combined with procedural rule flows, if-then rules and scorecards, increasingly complex credit risk strategies were implemented. It became clear that these approaches didn’t scale well and that using them to automate a modern credit risk strategy was complex, fragile, and expensive.
In recent decades, decision models and decision tables have established themselves as the best approach for managing complexity in decision automation. It’s time for credit risk organizations to adopt decision models and decision tables and refactor their aging decision trees.
In this whitepaper we demonstrate visually the benefits of refactoring your large decision trees into decision models.