The ability to design and execute credit decision strategy using results from past decisions, as well as insight from historical data, is a critical core competency for successful businesses. Carefully managed and executed rules help businesses achieve management goals, reduce errors and inconsistencies, and improve compliance.
The ARGO Decision Engine provides a tailored framework for making data-driven statistical inference and rules-based business process decisioning. ARGO performs historical analysis and modeling to encode best practices as rule sets and policies, enabling your businesses to improve consistency in decision-making while incorporating insight gained from data analysis. This approach will improve those decisions as your business grows and changes.
The Decision Engine provides tools that remove subjectivity and automate the decisioning process to:
- Identify risk
- Maximize return
- Enforce internal business rules and policies
- Integrate industry-standard scorecards
- Provide compliance and policy tracking
- Measure internal and external scorecard performance
- Utilize specific product characteristics.
Give Business Users Powerful Tools for Decision Modeling
The Decision Engine editor is a data-driven analytical authoring tool used for defining, testing, and documenting decision process flow.
With the editor, business users create structures called decision trees that automate decision making processes. Decision trees are logic testing flows in a graphical representation where variables or subjects are tested against business rule conditions. The test results direct the next sequential branch for subsequent testing, mathematical computation, or action within the same tree or linking to a different tree.
Improve Credit Risk Decision-Making Process with
The basic premise of ARGO Champion-Challenger model testing is to take a potential set of decision criteria (the challenger) and apply it against a separate set of decision criteria currently in use (the champion) with an objective to improve the decision-making process.
In Champion Challenger, an organization takes the results of the ongoing validation effort and establishes an alternative (challenger) decision process. The challenger is processed either simultaneously with current processes or in a batch environment against a recent period.
If the impact of the change is positive, then all or portions of the challenger are adopted or run in the current decision environment with certain override capabilities based on the challenger results. Understanding the effects of changes to modeling only comes with testing and analysis of how the new results fit into the organization’s goals.
Expand Decision Tree Sophistication Using Complex Expressions and Blended Data Sources
External data is mapped to data fields within the Decision Engine. The Decision Engine uses open database connectivity (ODBC) for retrieving data and supports the following database tools:
- Microsoft SQL Server
- Microsoft Access
- IBM DB/2