Understanding Decision Data

Decision data consists of the checks and scoring that were done by the system to determine the eligibility of a user. The decision data is a compilation of all modules configured within the Oraculi decision model of the lender. The decision model is what drives the checks and scoring done and the result is displayed as the decision data.

Understanding the decision data before approving a loan is very important as it shows you all checks your user passed and to what degree for some. With the decision data, you can still decide to decline a loan if some details are not acceptable to you.

The decision data is also displayed for failed loan requests which will also help in investigative checks to determine why a user was deemed ineligible.

Understanding a user’s decision data

The decision data has been optimized in an easy to understand graphical format.

High level decision data is first shown where you see if the user passed or failed the decision model.

  1. General Details: This is the high-level summary of a user’s loan request. It shows;

    1. Pass status (true or false depending on whether the user passed the checks or not),

    2. the overall decision (eligible or not eligible) 

    3. the engine used for the decision model which is typically Oraculi 

    4. the user’s credit score (which is determined by the scoring service within the Oraculi decision model) etc.

    5. The offer amount presented to the user if the user was eligible

    6. Failed Reasons: For requests that failed and the user was deemed ineligible, the failure reason is displayed here.

This is then followed by a breakdown of each individual check where you can see which checks were passed or failed. The categories are visually distinguished using color-coded borders – red indicating areas of failure and green highlighting sections that have passed – enabling swift identification of the results.

These sections can be further expanded by utilizing the 'show more' button, revealing precise details of the specific points of success or failure. Within this segment, users can review individual attribute sets along with their corresponding outcomes for each category. This information is also visually enhanced with color coding, distinguishing between passed and failed elements.

Please note that the raw JSON data can also be seen by choosing the ‘Show Raw Data’ button.