How to check why a user’s loan request failed

In lending operations, not every loan application results in approval. Borrowers may be declined for several reasons, including failing to meet eligibility criteria, having a low credit score, exceeding exposure limits, or already having active loans. Understanding exactly why a loan was rejected is critical for improving credit decisions, refining risk models, and providing transparency to both lenders and internal teams.

Lendsqr provides a structured way to diagnose failed loan applications using Decision Data. This allows lenders to inspect the exact reason a loan request did not pass the evaluation stage and identify which rule or condition triggered the failure.

This guide explains how to access decision failure information, where to find it in the admin console, and how to interpret the results effectively.

Understanding loan decision outcomes

Every loan application processed in Lendsqr goes through a decisioning system that evaluates the borrower against a set of predefined rules. These rules may include credit scoring thresholds, repayment history, income verification, blacklist checks, or exposure limits.

When a loan is approved, it means the borrower passed all configured checks. However, when a loan fails, the system records detailed information about the decision path, including which rule caused the rejection.

This is important because loan rejection is not random. It is the result of structured evaluation logic designed to ensure responsible lending.

For example, a borrower may be declined because their credit score is below the required threshold, because they already have an active loan, or because their transaction history does not meet the lender’s minimum requirements. Each of these outcomes is captured in the decision data for review.

Why reviewing loan failure reasons matters

Understanding why a loan request failed is essential for improving lending performance and customer experience.

From a risk management perspective, it helps lenders validate whether their credit rules are functioning as intended. If too many good-quality borrowers are being rejected, it may indicate that the rules are too strict or incorrectly configured.

From a customer experience standpoint, clear rejection reasoning helps support teams explain outcomes to borrowers and reduce confusion or frustration.

It also supports model optimization. By analyzing failure patterns, lenders can refine credit risk rules, adjust thresholds, and improve approval rates without increasing risk exposure.

Finally, it strengthens transparency and auditability. Every decision is traceable, making it easier to review historical loan outcomes when needed.

Step 1: Log into the admin console

Begin by logging into the Lendsqr Admin Console using your authorized credentials.

Once logged in, you will have access to customer management, loan tracking, and decision analytics features depending on your assigned role permissions.

Step 2: Go to Loan Requests

From the side navigation, under Request management, click Loan Requests.

This page lists every loan request, grouped into Pending, Declined, Failed, and Abandoned tabs. Click Declined or Failed to see requests that were not approved, or use the search box to find the specific borrower’s request by name.

Step 3: Open the loan request

Click the loan request you want to review. This opens its detail page.

At the top of the page, a row of summary chips gives you an immediate answer to “why did this fail.” Each chip shows one check the request went through, such as Credit assessment, ID image check, Selfie BVN check, Repayment methods, and Documents, along with whether it passed or failed. A chip like “Credit assessment 1/2 failed” tells you at a glance which category of check brought the request down, before you even open the full decision breakdown.

Step 4: Review Decision Data

On the Overview tab, scroll down past Financial overview, Loan Schedules, Borrower Information, and Cards and Bank Accounts to the Decision Data section.

This section contains the structured output of the decisioning engine (Oraculi) that evaluated the loan request: whether the engine itself passed the applicant, the decision it reached, and its scoring figures. Below that, expandable sub-sections such as karma and whitelist hold the results of other rule checks the request went through. Click “Show more” on any of these, or “Show Raw Data” on the Loan Decision card, to see the full detail behind a specific check.

Keep in mind that the decisioning engine result and the overall request status are not always the same thing. A request can show “Passed” and “Eligible” in Decision Data and still be declined overall, if it failed a different check, such as having no verified repayment method. Read the summary chips from Step 3 together with Decision Data to get the complete picture.

Read more on what our Oraculi credit risk rule settings are

How to interpret loan failure reasons

Loan failure reasons are structured to reflect the decision path taken by the system.

A common reason may indicate that the borrower failed a credit score threshold. In this case, the system is signaling that the borrower’s risk profile did not meet minimum requirements.

Another common reason is overlapping loan exposure, where a borrower is already servicing another active loan. This helps prevent over-lending and reduces default risk.

Some failures may come from custom rules configured by the lender, such as employment verification failures or income mismatches.

By interpreting these results correctly, lenders can distinguish between system configuration issues and genuine borrower risk.

Common mistakes when reviewing decision data

One common mistake is focusing only on the final rejection without reviewing the full decision path. Decision Data often contains multiple layers, and understanding the full context is important.

Another issue is assuming all rejection reasons are static. In reality, decision rules may evolve over time, so lenders should always check the current configuration when analyzing outcomes.

Lenders should also avoid using failure reasons in isolation without considering broader borrower history or portfolio trends.

Conclusion

Checking why a loan request was not successful is a critical part of managing a healthy lending operation. Lendsqr’s Decision Data feature provides full visibility into the evaluation process, allowing lenders to understand exactly why a borrower was declined.

By reviewing loan failure reasons through the admin console, lenders can improve credit policies, enhance credit risk rules, and ensure more accurate lending outcomes over time.

When used effectively, this feature not only improves operational transparency but also helps lenders build smarter, more adaptive credit systems that balance risk and customer experience.