Credit Scoring Features for BNPL Providers

Accurate credit scoring is vital for Buy Now, Pay Later (BNPL) providers, as it directly influences both risk mitigation and the accessibility of their services for consumers.

RiskSeal offers an alternative credit scoring solution for BNPL providers. With our experience in solvency assessment, we’d like to share risk management insights to help your BNPL business succeed.

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Overview of BNPL (Buy Now, Pay Later)

Since 2019, the global BNPL market has expanded tenfold, surging from $34 billion to $349 billion. Furthermore, forecasts predict it will reach $834 billion by 2028.

This trend is driven by the growing demand for the Buy Now, Pay Later service. According to recent industry research, 29% of online shoppers use this service at least once a month.

An additional 47% of respondents stated that they pay for half or more of their purchases using BNPL. 

Another impressive forecast comes from Adobe Analytics experts, who predict that during this year's holiday season, Americans will spend on BNPL purchases a record $18.5 billion.

The growing popularity of installments is leading to an increase in overdue payments on BNPL transactions. 

A recent survey conducted for Bloomberg revealed that 43% of Buy Now, Pay Later customers have overdue payments.

In most cases, the emergence of problematic debt is due to a lack of funds or simple forgetfulness. 

However, there are also fraudsters among BNPL clients. According to FICO data, 23% of synthetic identity fraud cases occur within the BNPL sector.

A responsible approach to credit decisioning plays a crucial role in reducing default rates and combating fraud for Buy Now, Pay Later providers.

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Key credit scoring challenges for BNPL providers

The specifics of the industry lead to certain challenges for BNPL providers in credit scoring.

Here are the main ones.

Fast approvals with minimal data

To meet user expectations, Buy Now, Pay Later providers must offer instant decision-making for transactions. This doesn't allow for a thorough examination of the applicant.

Additionally, a minimal set of data is required from the potential borrower. As a result, it can be difficult to gain a comprehensive understanding of their creditworthiness.

High risk of never-pays

According to eMarketer, most BNPL customers are from Gen Z, meaning they are young people under the age of 27.

They often lack sufficient credit history to make an informed decision on the application.

Providers operating in developing markets also face significant challenges. This is because there is a low level of banking service coverage.

In some of developing countries, over 60% of the population remains unbanked or underbanked:

Balancing user experience and risk management

Finding the balance between a smooth approval process and effective credit risk management is crucial for BNPL providers.

Here’s why this is so important.

1. Simplicity and speed are among the main advantages of Buy Now, Pay Later services.

Customers expect instant decisions without long processing times or the need to submit large amounts of documentation.

If this expectation is not met, the customer may choose an alternative payment model.

2. While ensuring a smooth approval process is important, providers must not neglect BNPL credit risk management. 

This is necessary to reduce default rates and the likelihood of fraud.

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Essential credit scoring features for BNPL providers

To meet user demands and avoid significant losses, BNPL providers should adopt a unique approach to credit scoring.

This approach includes using alternative data, behavioral insights, and innovative technologies to assess consumers.

Alternative data through digital footprint analysis

Turning to a progressive scoring platform, such as RiskSeal, allows using digital footprint analysis for BNPL. 

This helps assess the applicant’s creditworthiness and detect early signs of fraud.

Digital footprints are a valuable alternative data source, providing access to types of alternative data such as:

1. Information from email lookup. By knowing the applicant's email, you can gather:

• Email activity and validity
• Age of the email address
• Presence of linked online accounts
• Domain information
• Data breach incidents
• Inclusion in blacklists

2. Phone number lookup data. A phone number can reveal a lot about its owner, such as:

• Use of suspicious numbers (temporary phones or virtual SIM cards)
• Discrepancies between the IP address and operator code
• Presence in blacklists or high-risk databases

3. Borrower location data. Alternative data can also come from IP address lookup, which reveals:

• Location at the time of application to the BNPL provider
• Use of IP blockers (VPN, proxy, TOR, etc.)
• Type of IP address
• Inclusion in blacklists

4. Social media activity. Social profiles can provide insights into:

• Account type (personal/public, open/closed)
• Posted content, including geolocation tags
• The type of social network based on its purpose — entertainment (TikTok, Facebook) or professional (LinkedIn), etc.

5. E-commerce transaction data. For BNPL providers, important information includes:

• Frequency of purchases
• Payment amounts
• Types of items purchased
• Payment timing
• Payment methods
• Rate of returns and abandoned carts

6. Paid subscription data. Analyzing this type of online transaction reveals:

• Subscription costs
• Duration of subscriptions
• Payment regularity
• Subscription status changes (upgrades/downgrades)

Behavioral data

This concept encompasses the financial and consumer habits of borrowers. Analyzing this data can help BNPL providers objectively assess applicants’ creditworthiness.

When making installment decisions, it is essential to pay attention to such alternative credit data:

1. Applicant’s location. A mismatch between the stated residence and the geolocation detected can be a sign of high fraud risk. A sudden change in the borrower’s location can also raise suspicions.

‍2. Time of BNPL purchase. Purchasing goods late at night might indicate impulsiveness and a tendency for thoughtless spending.

3. Changes in shopping habits. Any purchases that are unusually large or different from typical spending can suggest fraud. For example, unusually large purchases, money transfers, and other such behavior could be alarming.

4. Connection patterns. Any attempts to conceal true personal information may be signs of fraudulent intent. Fraudsters often use IP anonymizers, virtual SIM cards, or burner phones.

Behavioral analysis allows for assessing an individual's creditworthiness, even without a traditional credit history.

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Machine learning models for real-time scoring

Artificial intelligence and machine learning enable BNPL providers to create adaptive scoring models that adjust to market changes.

Here are the key ways these technologies are used in RiskSeal’s scoring system:

1. Face recognition. This method compares user photos to determine whether the same person appears.

BNPL services can use this to reduce synthetic identity fraud cases and optimize the Know Your Customer (KYC) process.

2. Name matching. This technology compares the name on the loan application with names found in the applicant’s online profiles.

Discrepancies may indicate an attempt to conceal true identity.

3. Anomaly detection. AI in credit risk allows for the analysis of user data to identify patterns and detect deviations from normal behavior.

Any such deviation can be considered a sign of fraudulent intent.

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How can RiskSeal help BNPL providers?

RiskSeal helps BNPL providers optimize credit scoring. Let's explore how the platform addresses key challenges in this field.

Fast approvals with minimal data

RiskSeal finds over 300 data points on applicants without information in traditional credit bureaus.

High risk of never-pays

With our credit scoring, we almost flawlessly determine the probability of default. According to our data, BNPL companies experience a 25% drop in defaults within the first 3 months after integrating our data into their scorecards.

Balancing user experience and risk management

Our verification process takes up to 5 seconds. We instantly provide hundreds of data points and a ready digital credit score.