Types of Alternative Credit Data Available through Digital Footprint Analysis

For decades, lending organizations have relied on credit history to assess borrowers. 

This approach has shown clear weaknesses, such as limited financial inclusion, low predictive accuracy, and weak anti-fraud performance.

The solution is to use alternative data for credit scoring. 

At RiskSeal, we focus on gathering alternative credit data using digital footprint analysis. So in this article, we’d like to share insights on the potential of alternative credit scoring based on our experience in the field.

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Definition and use cases of alternative data

Alternative data in the context of credit scoring is non-traditional information that is used to assess a person's creditworthiness. 

Alternative data may include:

• Information about paying utilities, rent, and making other recurring payments.
• Activity on social media and other online resources.
• Employment and education information.
• Data on the use of mobile operator services.
• Online transactions, e.g. payments from digital wallets or purchases on e-commerce platforms.

Cases of alternative credit data use by financial organizations:

• Assessment of solvency of unbanked population.
• Optimization of risk assessment.
• Increasing the efficiency of fraud control.
• Development of personalized credit products.

To learn more about what is alternative data, check out one of our previous articles.

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The importance of analyzing digital footprints in credit scoring

A digital footprint is a set of data that remains on the web as a result of a user's online activity. 

Accounts, location data, and profile photos are digital footprints that lenders can use to get a clearer picture of an applicant.

How can digital footprint analysis help with credit scoring? 

It gives lenders access to a variety of information about a borrower:

Solvency. Specific details can reveal a borrower's financial standing. For instance, using premium services, paid email hosts, or iOS devices can be positive indicators.

Behavioral insights. By analyzing digital footprints, lenders can understand behavioral patterns that may indicate a risk of default.

For example, irregular subscription payments suggest financial indiscipline. Signing up for gambling platforms indicates a tendency toward high-risk behavior. Making frequent online purchases late at night may signal impulsive spending habits.

Reliability of the data provided. Digital footprint analysis enables you to detect subtle inconsistencies between the information submitted in the application and actual data.

For instance, multiple online identities linked to the same user may signal potential data falsification. Similarly, a mismatch between the stated place of residence and the device's IP address could indicate suspicious behavior.

These alternative credit data sources are highly productive in assessing credit risk. 

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Types of alternative credit data

Through digital footprint analysis, lenders have access to these types of alternative data:

Email lookup data

With just an applicant's email address, lenders can perform email lookup and access valuable insights:

• Check if the email is active and deliverable.
• Identify any linked accounts and see how long the inbox has been active.
• Analyze the email domain, type, and check for data leaks.
• Check if the email is listed on any blacklists.

Phone number lookup data

The phone number lookup can be done using the applicant’s phone number. It allows lenders to:

• Identify disposable numbers, burner phones, or virtual SIMs.
• Detect mismatches between the phone’s operator code and IP geolocation, which may indicate fraud.
• Check if the number is on any blacklists or other databases, which can raise the risk level.

Location insights

IP address lookup provides additional alternative credit data into the applicant’s geolocation and online behavior:

• Verify the applicant’s location at the time of application and flag any discrepancies.
• Detect the use of anonymizers like VPNs, proxies, or TOR.
• Identify the type of IP address and check if it appears on blacklists, which could signal potential fraud.

Social media activity data

Social networks offer valuable data on the borrower:

• Confirm registration with a phone number and email, as the average U.S. resident has around 7 social media profiles. A lack of social presence could indicate an attempt to conceal the digital footprint.
• Analyze profile type (public or private), shared content, and geolocation tags to verify the applicant’s stated place of residence.
• LinkedIn profiles provide insights into employment and education, positively influencing the digital credit score.

E-commerce activity data

With 2.7 billion people shopping online, e-commerce activity data covers about 33% of the global population:

• Purchase frequency, spending patterns, and types of goods bought are key indicators of creditworthiness.
• Analyzing transaction times, payment methods, returns, and abandoned carts provides additional insights into consumer behavior.

Paid subscriptions data

Paid subscriptions are a positive indicator for lenders, but further analysis is needed to understand the borrower’s financial situation:

• Evaluate the types, costs, and duration of subscriptions, as well as the consistency of payments.
• Changes in subscription status, such as upgrades, downgrades, or cancellations, can signal shifts in the applicant’s financial stability.

Name variations

Modern alternative credit data providers perform Name Matching to verify consistency across different sources:

• Discrepancies in names can suggest attempts to falsify personal information.
• Variations in name spelling due to linguistic or cultural differences.

Avatars

Face match technology uses facial biometrics to compare multiple photos of the borrower:

• Detects discrepancies that may indicate identity fraud.
• Can analyze profile pictures from online accounts and compare them with customer selfies provided by the lender.

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Key challenges in using digital footprint data for credit scoring

No doubt analyzing digital footprint data brings many benefits to credit organizations. However, the process can be challenging.

Data privacy and security

Collecting customer data entails the challenge of ensuring its confidentiality. 

The lender must implement strict security measures to prevent their misuse or leakage.

Regulatory compliance

Each jurisdiction has its legal regulations regarding the protection of personal data. 

For example, in Europe, lenders must meet GDPR compliance requirements. This allows them to comply with fair lending regulations and use customers' personal information lawfully. 

Bias and discrimination

Digital footprint data can introduce algorithmic bias.

This must be taken into account to avoid unintentionally discriminating against certain demographic groups.

Data quality and reliability

It is important for lenders to respect the accuracy of alternative credit scores.

To do this, the consistency, reliability, and relevance of digital footprint data should be monitored.

Lack of standardization

Digital footprint data from different sources are often not standardized. This makes them difficult to integrate and interpret.

Transparency and explainability

Alternative credit scoring models are typically “black box” in nature. That is, their solutions are impossible to understand and interpret.

Firstly, it contradicts the regulatory requirements of the regulators. Second, it reduces stakeholder and borrower confidence.

Consumer consent and awareness

Many applicants do not fully understand the ways and purposes of processing their data. This may raise ethical concerns about the transparency of their use.

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Advantages of using digital footprint data with RiskSeal

The RiskSeal Digital Credit Scoring system provides its clients with all types of alternative credit data discussed in this article.

With their help, it is possible to achieve the following results:

1. Financial inclusion. You can lend to people even if there's no information about them in credit bureaus. Our clients have doubled their approval rates within the first three months of using the data.

2. Improved predictive power of credit models. Using alternative data allows us to accurately identify unreliable customers. Our clients see a 25% reduction in defaults. 

3. Improved customer experience. We gather over 300 data points for each borrower and provide a ready-to-use Digital Credit Score in just 5 seconds.