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Understanding a user’s decision data
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Decision data is a structured record of all the checks and scores the system ran against a borrower’s loan request to determine whether they are eligible for a loan. It is generated by Oraculi, Lendsqr’s credit decision engine, and is available on every loan request, whether approved or declined, directly from the admin console.
What is decision data?
Imagine a lender who receives a loan request from a borrower who has been on the platform for three months and is now asking for a larger amount than they have previously taken. Before approving, the loan officer opens the decision data. The borrower passed the identity and blacklist checks but scored below the threshold on the affordability module. Rather than an outright decline, the loan officer approves a smaller amount, a decision they can justify and document clearly. This is exactly what decision data is built for: informed, evidence-based lending.
Think of it like a report card for a loan application. Instead of just seeing a pass or fail, you see every subject that was tested, the score in each, and exactly where things went wrong if they did. This makes it far easier to approve confidently, decline with justification, or investigate a disputed outcome.
What decision data shows you
Decision data is organized into four sections, each surfacing a different layer of the evaluation.
General details
This is the high-level summary of the loan evaluation. It shows the pass status (true or false), the overall decision (eligible or not eligible), the decision engine used (typically Oraculi) and the borrower’s credit score as calculated by the scoring module within the decision model. This section gives you an immediate read on the outcome without needing to dig into individual checks.
Offers
If a borrower is deemed eligible, this section displays the loan offer generated by the system. The offer amount and terms are based on the borrower’s request and the offer settings configured within your decision model. What appears here is what the system determined the borrower qualifies for under your configured rules.
Oraculi
This is the core of the decision data. It shows the individual checks the borrower went through as part of the decision model evaluation, run sequentially. If a borrower fails one module, the process stops there and subsequent checks are not run.
Checks within this section include the Karma check (blacklist verification across the Lendsqr network), ecosystem or Loci checks (historical behavior data across the platform or your specific organization), scoring modules (weighted attribute scoring based on factors like age, employment, and location), and credit bureau or bank statement checks where configured.
Each check is color-coded: red indicates a failure, green indicates a pass. You can expand any section using the “Show more” button to see the specific attributes evaluated and their individual outcomes. To view the complete raw output, select “Show raw data”, which returns the full evaluation in a structured data format.
Reasons
If a borrower is ineligible, this section displays the specific reasons the system returned for the failed evaluation. Rather than inferring why a request failed from the Oraculi section, the reasons section surfaces failure explanations directly, making it straightforward to communicate outcomes to borrowers or to investigate disputed decisions.
For routine approvals, a quick check of the general details section is usually sufficient. If the pass status is true and the offers section shows a valid loan amount, you have what you need to move forward.
For borderline cases, expand the Oraculi section to review individual check results. The system may have returned an eligible result, but specific module scores may give you reason to pause. Decision data supports your judgment — you are always free to decline a request the system has marked eligible if the underlying data warrants it.
For failed requests, start with the reasons section to understand why the borrower was turned down, then cross-reference with the Oraculi section to identify exactly where the evaluation stopped. This is especially useful when a borrower contacts you to dispute a decision.
Common errors and what they indicate
If a borrower fails at the Karma module, it means they have been flagged on the Lendsqr network for a previous default or compliance issue on another platform. This is not a system error — it is the decision engine working correctly.
If the decision data shows a failed status but the reasons section is empty, this may indicate a configuration issue with your decision model. Review your model settings on the admin console to confirm all modules are correctly set up.
If the credit score is missing from the general details section, the scoring module may not have been included in your decision model configuration. You can add or adjust scoring modules from the decision models section of the admin console.
