Digital Footprint Software
for Alternative Credit Scoring

It works by using digital footprint analysis to predict repayment or default.

Digital footprint-based credit scoring gives lenders a clearer view of stability and intent. Even when traditional credit data is limited.

Modern scoring systems pull signals from a wide range of internet resources. These can include social media, messengers, e-commerce sites, entertainment services, travel apps, professional networks, and online education platforms.

Each environment adds small clues about how established, consistent, and active a digital identity is. The scoring system brings these signals together and compares them to patterns seen across millions of past applications.

Certain behaviors align with lower risk, such as long-standing accounts and steady, consistent online identities. Others point to higher risk, like brand-new accounts or fragmented, irregular activity.

The result is a credit score or risk estimate lenders can use when evaluating applicants.

RiskSeal’s digital footprint lookup analyzes data tied to email, phone, IP, location, and online accounts. 

Each applicant is evaluated through 400+ real-time signals that show how they engage across the digital world. These include:

-Email intelligence. Email validity, age, domain type, breach history, and linked accounts across major platforms. This helps distinguish long-standing identities from new ones.
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-Phone number lookup. Number validity, age, carrier details, blacklist status, and presence on messengers like WhatsApp or Telegram. This reveals if the number is real, active, and tied to a consistent user.
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-Social media presence. Signals from platforms like Facebook, LinkedIn, and Instagram that show digital maturity, identity consistency, and real-world presence.

-E-commerce engagement. Activity across Amazon, eBay, Walmart, Mercado Libre, and similar sites that reflects purchase habits, account age, and financial behavior.

-Paid subscriptions. Subscription patterns on services like Netflix, Disney+, and Spotify that show budgeting discipline and ongoing financial responsibility.

-Web services and tech accounts. Insights from accounts with Apple, Google, Zoho, and other cloud services that reflect depth of digital engagement.

-IP signals. IP address details, connection types, and geolocation checks that help identify mismatches and high-risk access patterns.

We also provide AI-based identity verification, including name matching and face recognition across avatars. That way, we validate the consistency of the applicant’s information across platforms.

In short, we turn everyday identifiers into powerful insights about authenticity, stability, and intent.

RiskSeal analyzes signals from 200+ online platforms where users already maintain an active digital presence.

These include social networks, messaging apps, e-commerce sites, entertainment apps, travel platforms, digital services, professional networks, and online education resources.

We look at how applicants behave across these ecosystems to understand digital maturity, consistency, and intent.

We also analyze real-time signals tied to email, phone, and IP. These identifiers reveal activity history, account age, presence on messaging apps, geolocation consistency, and other patterns that help confirm identity strength.

Our digital footprint AI analysis uses only signals that applicants already make visible when interacting with third-party platforms.

No privacy invasion. No hidden scraping. Just transparent, compliant data enrichment based on public and consensual digital activity.

Never.

Our digital footprint analysis tools only process information that users have willingly shared with other platforms. For example, email registrations, social profiles, or business directories.

We also never collect data that reveals protected attributes like religion, gender, or ethnicity. Only legitimate digital traces, not private messages or sensitive content.

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Yes.

We offer a free Proof of Concept to demonstrate how our digital footprint software strengthens your credit scoring models.

You’ll see real data in action before making any commitment.

Just fill out the contact form to book a demo. We’ll follow up with a personal consultation, no strings attached.

During that call, we’ll walk you through how our digital footprint tracking software integrates seamlessly with existing credit risk systems.

Yes, but only what is public, open, and fully consensual.

RiskSeal does not read private messages, posts, comments, or any content hidden behind privacy settings. We never access credentials, inboxes, friend lists, or anything that requires logging in.

Our social media footprint analysis relies only on public signals that users already make visible on third-party platforms.

These include basic profile details, account age, platform presence, and whether the applicant maintains a consistent identity across networks.

Such open-source signals help lenders confirm that the digital identity behind an application is stable without touching any sensitive or private data.

We use standard predictive metrics such as AUC. In our validation tests, the final RiskSeal digital scoring model reached an AUC of 0.67 on an independent dataset.

This shows stable predictive power in separating higher-risk borrowers from lower-risk ones using digital footprint assessment alone.

Digital signals work best when paired with traditional credit data.

In our observations across multiple lending portfolios, bureau-only models often perform in the upper-0.60s AUC range, while the combined model reaches 0.73 AUC.

This uplift shows that digital footprint insights add context that bureau scores alone may miss, especially for thin-file or new-to-credit applicants.

Client outcomes follow a similar pattern. Many lenders report higher approval rates and lower defaults after adding RiskSeal’s digital score to their workflow.

Results vary by market and portfolio, but the trend is consistent: combining bureau data with digital signals leads to more accurate decisions and cleaner portfolios.