Managing loan requests in Lendsqr is very advanced and automated. As a lender, you only get to see loan requests from the best customers as the system handles the most difficult of decisions for you.
Once in a while, a few customers would engage with you and complain about not getting their loan requests approved. Over time, we’ve seen tons of different reasons why loans get declined but these 10 reasons are the commonest.
Customer is found in Karma blacklist
When customers request loans from a Lendsqr powered lender, their details, such as phone, BVN, email, are run against Karma, our one of the largest blacklist services in Nigeria. If any of their records is found, When a customer loan request fails because they are found in Karma, it simply means at one time, a lender has reported them for fraud or chronic unpaid loan.
If a customer is bounced on Karma, rejoice as they are not the type to be your customers.
However, if it’s confirmed that the customer has been unduly blacklisted, you are able to whitelist them for loan pre-qualification.
Read more about how whitelisting works.
Too many failed loan requests
To prevent nuisance and some bad customers gaming the system, a lender’s loan setting could have a maximum cap of number of loan requests that a customer can make within a specific period. For example, if a lender has set 100 loan requests as the maximum a customer could make over a week period, the system will automatically start declining their 101st loan requests for these reasons.
As a lender, you would need to make a decision about how much nuisance they can handle from customers' many loan requests and set their threshold accordingly.
However, if it’s confirmed that the customer has been unduly declined and you want to give them a loan without tampering with your settings, you are able to whitelist them for loan pre-qualification.
Read more about how whitelisting works.
Previously declined by the lender
As a lender, if you have previously declined a customer’s loan request at one time for different reasons and you don’t want them to start reapplying again when you know that their situation hasn’t changed, this setting is there in the system to block such customers from being able to make new loan requests.
It’s almost like a nuisance prevention setting like the previous one.
However, if it’s confirmed that the customer has been unduly declined and you want to give them a loan without tampering with your settings, you are able to whitelist them for loan pre-qualification.
When the borrower has a running/active loan
When a borrower already has an existing loan that is currently active or ongoing, it can impact their ability to take on additional debt.
As a lender, you probably consider the borrower's debt-to-income ratio, which measures the proportion of their income that goes toward paying off existing debts. If this ratio is too high, granting another loan may pose a higher risk of default or financial strain for the borrower.
However, if you are sure that you want to give them a loan without tampering with your settings, you are able to whitelist them for loan pre-qualification. Read more about how whitelisting works.
The borrower failed the Credit bureau check
Credit bureaus collect and maintain credit information about individuals, including their credit history, payment behaviour, and outstanding debts. Failing a credit bureau check indicates a poor credit history, such as missed payments, defaults, or high levels of debt, which makes the borrower a higher risk for lenders. The system automatically declines a loan which fails Credit bureau checks.
As a Lender, you can view the decision data to identify the exact reason for the credit bureau check failure.
However, if you are sure that you want to give these borrowers a loan without tampering with your decision settings, you are able to whitelist them for loan pre-qualification. Read more about how whitelisting works.
The borrower has been credit delinquent
When a borrower becomes delinquent on their credit, it means they have fallen behind on their required payments and have not fulfilled their financial obligations. When a borrower does not make the required payments within the specified time frame, they are considered delinquent.
Delinquency can have adverse effects on a person's credit score and overall creditworthiness. This behaviour raises concerns about their financial responsibility and their ability to repay new loans.
When the system flags a borrower in this category, their loan requests are declined.
If you are sure that you want to give credit delinquent borrowers a loan without changing your decision settings, you are able to whitelist them for loan pre-qualification. Read more about how whitelisting works.
The borrower has paid a penalty before
Paying a penalty suggests that the borrower has violated terms or conditions related to a previous loan. This may indicate a pattern of financial mismanagement or difficulty in meeting loan obligations, The system detects that the borrower in question has paid a penalty and this leads to a decline in the current loan request.
Customers with this type of history must have delayed paying a loan amount. In the default decision model settings, the maximum value for these parameters in the ecosystem is zero by default as there should be no tolerance for such customers.
However, if the borrower has settled the loan and you wish to allow this loan request from such customers without changing your decision settings, you can whitelist them for loan pre-qualification. Read more about how whitelisting works.
Failed selfie BVN checks
This involves verifying the borrower's identity using a selfie and BVN in an attempt to match the identity. Failing this verification process may indicate an inconsistency or mismatch between the provided information and the borrower's actual identity, raising concerns about potential fraud or impersonation.
This is a red flag. A lender should not give loans to these borrowers. However, if you are sure that you want to give these borrowers a loan without tampering with your decision settings, you can request another means of verification or insist that they upload a better picture for selfie BVN verification.
You are also able to whitelist them for loan pre-qualification if you are sure you wish to give them a loan. Read more about how whitelisting works.
The borrower has changed their employment category more than twice
When a borrower makes frequent changes to their employment category more than twice for example, their loan will be declined by the system. Constantly changing employment categories can be viewed as a lack of stability and may raise concerns about the borrower's income stability, job security, and ability to maintain a stable income to repay the loan.
The maximum limit is currently set to 2 in our default decision model. The lender can modify these settings to their preference.
However, If you are sure that you want to give these borrowers a loan without changing your decision settings, you are able to whitelist them for loan pre-qualification.
The borrower changed their income category more than twice in a month
Similar to changes in the employment category, frequent fluctuations in income can indicate instability or irregularity in the borrower's financial situation. As a lender, you prefer borrowers with a consistent and reliable income source, making frequent income category changes is a red flag and is a reason for loan request decline.
The Total number of allowed income increments maximum limit is currently set to 3 in our default decision model. Therefore, if a borrower changes their income category more than 3 times in the last 30 days, the system declines their application.
As a lender, you can increase this limit in the decision settings, however, if you are sure that you want to give them a loan without tampering with your settings, you are able to whitelist them for loan pre-qualification. Read more about how whitelisting works.
The borrower failed scoring
Various scoring models are used by lenders to assess the creditworthiness and risk profile of borrowers. The Lendsqr scoring model uses information such as; age, marital status, monthly income, sector of employment, location, etc to assess a borrower’s eligibility.
For instance, you can configure a loan product to be accessible by only borrowers within a certain age range or work in the specific sector of employment which you configured.
Failing the scoring process indicates that the borrower's financial profile does not meet the lender's predefined criteria or falls below a certain threshold, resulting in a system decline in their loan request.
When a borrower fails scoring, you should check the decision data to determine the particular reason the scoring failed after which you can decide to modify scoring the criteria in the decision model settings to accommodate this lender or Whitelist the borrower.