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Thin File
Discover how alternative data, like digital footprints, helps lenders assess creditworthiness for thin credit files.
Discover how alternative data, like digital footprints, helps lenders assess creditworthiness for thin credit files.
The majority of banks rely on traditional credit scoring models to assess their clients' creditworthiness.
This means they exclusively consider the borrower's credit history: the number of loans taken and repaid, adherence to repayment schedules, income-to-debt ratios, and more.
However, there are many consumers worldwide who either have no credit history or possess minimal financial data. These primarily include individuals without access to banking services, immigrants, and young people who have not yet opened credit accounts in a specific jurisdiction.
These individuals are referred to as “thin file,” and they are unable to secure loans under the traditional credit scoring approach.
To understand the connection between a limited credit history (“thin credit file”) and a low credit score, let’s examine how traditional creditworthiness evaluation works.
As an example, consider the scoring model of one of the largest players in the credit market — FICO.
To achieve a high FICO score, a consumer needs to have credit accounts and strictly adhere to payment schedules. Here's what their credit score depends on:
1. Payment history. The number of points earned depends on how timely the borrower has repaid previously issued loans. The system considers late and missed payments as well as any collection activities.
2. Current credit debt. This metric shows the amount a person owes to banks at the time of applying to a credit organization. The amount of debt is compared to the available credit limit. If the borrower is far from exhausting their limits, their credit score will not be negatively affected.
3. Length of credit history. The longer a consumer has been a client of banking institutions, the more likely they are to achieve a high credit score. The length of credit history is determined by the date of the first loan issuance.
4. Credit mix. A person with a diverse range of credit products has a better chance of obtaining a high FICO score. These can include retail loans, mortgages, credit cards, and more.
5. New credit. Having numerous new, unpaid loans can indicate an overwhelming financial burden. This increases the level of risk and, in most cases, negatively affects the credit score.
The diagram below illustrates the percentage importance of these factors in forming a FICO score:
As you can see, the FICO score, like many other traditional credit ratings, directly depends on the consumer's financial history.
Accordingly, thin file credit history serves as a basis for denying a loan due to insufficient data for credit decisioning.
Traditional scoring models have limitations, causing financial organizations to lose many potential clients and a significant share of profits. After all, there are 1.4 billion unbanked and underbanked people worldwide.
There is a way out of this situation: the use of alternative data in credit scoring.
Alternative data refers to information that cannot be obtained from credit bureau reports, meaning data unrelated to financial history and the credit scores derived from it.
Here are examples of non-traditional or alternative data:
It is worth noting that the analysis of digital footprints is one of the most informative sources of alternative data for online lenders.
This approach allows lenders to create a comprehensive profile of a potential borrower and issue loans for thin credit files.
This method of evaluating applicants' creditworthiness is precisely what the RiskSeal scoring system specializes in.
Analyzing digital footprints can provide lenders with access to the following information about potential borrowers:
Social platforms can reveal details about a borrower's employment and education.
Advanced technologies can determine whether the same person appears in profile photos (face match) and whether the same name is used across different resources (name match).
The data enrichment solution can provide all this information.
Certain behaviors, such as registering on online gambling sites or making impulsive e-commerce purchases (e.g., late at night), may negatively impact a borrower’s profile.
Positive factors include having paid subscriptions or using premium services, which can enhance a borrower’s digital credit score.
No credit repayment history? No problem — analyzing the timeliness of payments for online purchases, the amounts of money transfers, and similar activities can be just as insightful.
The first thing lenders should examine is whether the borrower’s email and phone number have a history of legitimate usage and are associated with their identity.
Checking if the email or phone number has been used in previous applications, whether they appear in trusted consumer databases, and their registration history can help assess credibility.
Lenders should also verify if the email or phone number is flagged in fraud detection databases, detect the use of temporary or disposable contact details, and analyze patterns such as the age of the email domain or phone carrier type.
Partnering with an alternative data provider is a reliable way to lend to consumers with a credit report thin file.
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