Biometric Credit Scoring See the Risk Your Data Can't

Powered by Trusting Social's proprietary computer vision, Vision Score is an independent risk layer validated on 1M+ real-world outcomes. Highly predictive. Unbiased by design. Built for the borrowers your model can't reach.
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Vision Score capturing biometric signals from a face scan during a loan application on a mobile phone
Discover how VisionScore scores thin-file and no-file borrowers with no extra steps for the applicant.

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vision-score-brochure

Your model knows their past. Vision Score reads their present.

Vision Score, by Trusting Social, is an AI credit scoring model that applies machine learning to the facial image already captured during digital onboarding, turning biometric signals into predictive credit analytics for Philippine lenders. Validated on 1M+ real loan outcomes, it scores thin-file and no-file borrowers with no extra steps for the applicant.
The Signal

Every borrower has an invisible signature

At the point of application, Vision Score captures real-time biometric signals—unique and invisible to traditional models

Older Filipina taking a selfie during a loan application as Vision Score detects a biometric signal and returns an independent score
The Proof

Identify risk that historical data misses

Vision Score flags hidden default risks in profiles that appear safe by every historical measure, reading the "present moment" signals that standard models were never built to see.

Credit analyst reviewing an application where Vision Score surfaced a hidden default risk
The Reach

Score the borrowers no one else can

For thin-file applicants, Vision Score analyzes onboarding cues—liveness, age, and environment—to generate a standalone risk score with no extra steps or infrastructure.

Young Filipina applying on her phone, a thin-file borrower Vision Score can assess
The Outcome

A more accurate, wider portfolio

By pairing historical data with real-time biometric signals, you surface hidden risk and score the "invisible" borrower. Fewer defaults, more approvals.

Older Filipino borrower approved after the lender combined their existing model with Vision Score
The AI Advantage

A new category of credit risk intelligence

At the point of application, every borrower produces a biometric signal — unique, real-time, and invisible to every traditional scoring model. Vision Score captures it and produces an independent credit risk score within your existing onboarding flow
Vision Score reading a digital onboarding capture — biometric signal, liveness and image integrity checks, then a risk parameter analysis
How Vision Score Works

Five stages. One score.

Every borrower produces a biometric signal at the moment they apply.

Stage 1

Biometric Signal Capture

Captured within your workflow

Stage 2

Liveness Verification

Deepfake, mask & spoofing detection

Stage 3

Risk Parameter Extraction

Validated against real loan performance data

Stage 4

Score Calculation

Trained and calibrated on your institution's data

Stage 5

Decision Integration

Integrated score into your lending workflow

Encrypted end-to-end
Unbiased by design
Data processing agreement
Gini index up to 0.63
Trained on 1M+ real world data
Designed for the credit invisible
Built on Data

The Numbers Behind the Score

These are not projected figures. Vision Score is a live product, validated on real loan performance data.
Real loan outcomes 1,000,000+
Vision Score is validated against more than one million actual loan performance records, not projections.
Gini index 0.63

Standalone predictive power: a sharper lens on credit risk than traditional scoring models, from a signal your model has never had.

Significant bias detected Zero

Unbiased and compliance-ready. Audited across race, gender and age, and built to satisfy BSP model-risk and Data Privacy Act (RA 10173) reviews.

Borrowers scored No-file
Designed for the credit-invisible: thin-file and no-file Filipino borrowers that bureau data and traditional models cannot reach, with no extra steps for the applicant.
Gini index measured on live Philippine lending portfolios. Results vary by segment; a pilot on your own data confirms the lift before rollout.
Filipino rice farmer checking his phone while standing in a paddy field

The borrowers your bureau file can’t see

Most Filipino adults have never held a credit card or a formal loan, so the bureau returns nothing on them. Not a poor score — no file at all. A rice farmer with twenty years of harvests behind him and a salaried applicant two months into her first job look identical in that lookup, and most models treat them the same way: decline.

Vision Score reads a signal that exists whether or not a bureau file does. It assesses the applicant at the moment they apply, from the capture your onboarding flow already takes, so a thin file stops being the end of the assessment. The question moves from whether you have history on this person to whether you can tell what kind of borrower they are — which is the question you were trying to answer in the first place.

Who it’s Built for

Any Philippine institution making credit decisions at scale

Digital Banks and Digital Lenders
Fast decisioning, high volume, thin-file borrowers. You are already comfortable with alternative data and your infrastructure is API-ready. Vision Score adds an independent signal to every application without adding friction — and the pilot cycle is short enough to produce results within a single campaign cycle.
Universal and Commercial Banks
Your scoring infrastructure is strong and your cycles are longer. Vision Score does not ask you to change what you have. It adds a data source your model has never had, and a pilot on your own portfolio shows the lift before anything changes in production.
Rural and Thrift Banks
Your borrowers are often invisible to every traditional data source. That is not a gap in your model. That is a structural limitation of the data. Vision Score was built for exactly this population — producing a credible independent risk signal where no other data exists.
It does not replace your scoring infrastructure. It adds an independent signal to it, and a pilot on your own portfolio shows the lift before anything moves into production.
The Full View Webinar Series

Vision Score, shown on real portfolios

Our risk and data science team walks through what the signal is, what it catches, and how a pilot runs. Three sessions, all on demand. Watch now Session 3 · On demand
Session 1 • On Demand The Biometric Signal Your Model Is Missing
Why the signal at the moment of application predicts risk that historical data cannot.
Filipino borrower checking his phone on a street in Manila, the moment of application Vision Score reads
Session 2 • On Demand Vision Score in the Field
A live walkthrough on real portfolios, including the hidden-risk cases a bureau file never shows.
Trusting Social risk and data science team reviewing Vision Score results on a laptop
Session 3 • On Demand From Compliance to Pilot
The questions compliance asks before a biometric score goes live, how a pilot is scoped on your own data, and what a lender actually sees at the end of it.
Compliance and risk officers reviewing a Vision Score pilot before it goes live
Frequently Asked Questions

Biometric credit scoring, explained

What is biometric credit scoring?
Biometric credit scoring uses signals captured at loan application, such as liveness, image integrity and the applicant’s environment, as an independent credit risk signal. Vision Score turns that signal into a 0 to 100 risk score that runs alongside your existing model.
How is Vision Score different from a telco credit score?

A telco credit score reads mobile usage history. Vision Score reads the biometric signal captured during eKYC at the moment of application, so it needs no borrowing history and no telco data. Many lenders run both for a more complete risk picture.

Is Vision Score fair and unbiased?

Yes. Vision Score is calibrated on your institution’s own loan outcomes and has passed 30 fairness checks. It is designed to add predictive power for thin-file borrowers, not to penalise any demographic group.

How do we pilot Vision Score?

Vision Score is validated on 1M+ real loan outcomes. Most engagements start with a short pilot on your own portfolio, so you see the lift on your data before any rollout. Book a demo to scope it.

Can a face really predict credit risk?
Not the way the question suggests. Vision Score isn’t based on physical appearance, and it isn’t trained to link physical characteristics to creditworthiness. Protected attributes such as race, ethnicity, skin colour and nationality are excluded by design. It reads non-sensitive properties of the onboarding capture, such as image integrity, consistency and resistance to spoofing, plus cues like liveness, age estimation and environmental context, all validated against real loan performance. Every update is tested under the EEOC Four-Fifths Rule (30 of 30 adverse-impact checks, zero violations), and it delivers a minimum 30% Gini as a standalone signal across 1M+ loan outcomes. You can back-test it on onboarding images you already hold. Read what Vision Score reads, and what it doesn’t.
More answers in our FAQ, or pair it with the Telco Credit Score.

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