SocialScore's Fintech Customer Intelligence Layer turns a single email or phone number into 300+ real-time signals for digital lenders, banks, payment companies and BNPL providers. Pricing starts at about €1 per verified profile, with volume pricing above 10,000 checks a month. A free demo and 500 free identity checks are available from SocialScore (DSNTech LTD, Sofia, Bulgaria).
You approve applications in seconds.
You still don't know who's asking.
The Customer Intelligence Layer that verifies, signals, and personalizes before the form is even submitted — faster onboarding, fewer synthetic identities, more approved good borrowers.
Every extra field costs you the applicant.
Every missing signal costs you the bad one.
That's the whole tension of fintech onboarding. In Europe, 68% of consumers have abandoned a financial-services application in the past year (Signicat, 2025), and banks worldwide report losing clients to slow onboarding (Fenergo). Document re-upload alone triples the odds someone leaves.
Identity, risk, marketing, and support usually run on different knowledge of the same customer. SocialScore gives every one of your systems the same signal — one customer, understood the same way everywhere. You take the ID data. We hand you the context.
What that costs, sector by sector
Thin-file and young applicants get declined not because they're risky — because there's no bureau file to see them at all. Marketing spend chases the wrong lookalikes, and manual review eats hours it doesn't have.
We give banks the context to evaluate an applicant in real depth — the same signal that speeds up onboarding, sharpens cross-sell targeting, and carries through to every later channel.
Every re-uploaded document triples the odds this applicant finishes onboarding somewhere else — and every hour spent re-checking a good customer is an hour not spent acquiring new ones.
Instant approval means fraud has to be caught in seconds, not days — and the same instant window leaves no time to target the offer to who's actually buying.
Everyone wants AI underwriting.
Nobody has context to underwrite with.
This isn't only a lending problem — it's the problem underneath every automated decision fintech has bought. Your credit model, your onboarding flow, your fraud rules all guess, because none of them know who's actually on the other side of the form.
Everyone chases approval rate
Without external signals, "risk-based pricing" means declining good thin-file applicants to protect a model that can't see them.
Everyone is adding AI decisioning
A credit model with no context treats a first-time applicant and a returning good customer exactly the same way. That's the default, not a bug.
Everyone fears synthetic identity
But nobody can tell in real time who's behind the form — real applicant, bot, or a fabricated identity built for fraud.
Bureaus only see people who've already borrowed —
we see everyone else too.
SocialScore enriches an email or phone into 300+ external signals — the same class of alternative data that fills the gap a credit bureau leaves for thin-file, young, or new-to-country applicants.
Data & Signals Engine
Email + phone + country → 300+ signals, under 5 seconds. Your own application data can feed it too — not just email and phone.
Software & Capabilities
Works on our signals — or entirely on your own data: application history, repayment behavior, your own risk segments. Use us purely as software, if that's all you need.
Your core banking system, LOS, and KYC/AML stack stay exactly where they are. SocialScore feeds the tools you already run with the context they were missing — it never makes the credit decision for you.
From an email address, phone, or web visit to a
verified applicant profile
Define the group
Top applicants, dormant accounts, new leads — any segment you choose.
Enrich & profile
300+ signals per identity, under 5 seconds. Batch mode for full lists.
Segment & predict
Behavioral segments matched automatically from external signals.
Act in real time
Every new applicant matched against your segments before the form is done.
No devs required —
this is the actual product
Everything below is live in the SocialScore dashboard — no integration needed to see it working.
Integration takes about 10 minutes. One API call returns every signal — as JSON for your systems, or CSV for a straight upload into a spreadsheet.
One call returns
six different kinds of answers
Verification & Health Signals
Data: deliverable/disposable/breach-exposed email, reachable phone, plus Social, Email, Phone, Fraud and Buyer Power signals.
Value: confirms this is a real, reachable person in the same call. At least 20% of a typical database carries invalid or fake contact data that tanks sender reputation and sends whole campaigns to spam — this catches it before the UYC document step even starts.
Interests
Data: 15 interest categories, benchmarked against the country average.
Value: tells growth teams what to actually offer and lead with — powers cross-sell targeting, not the credit decision itself.
Digital Footprint
Data: platform presence, account tenure, LinkedIn job title and employer.
Value: an income and stability proxy for thin-file applicants a bureau can't see at all — this is what unlocks previously-invisible good applicants.
AI Customer Persona
Data: a synthesized, ready-to-use persona built from every signal above.
Value: powers precise targeting to cut wasted spend on Google and Meta, plus personalization and segment-building — your growth team or AI acts on it immediately.
Human or Bot
Data: real applicant, a legitimate AI agent applying on their behalf, or a fraud ring mass-submitting applications.
Value: give the agent a fast, structured path instead of a form built for a human, so you don't lose that application — fraud rings still get sorted out before review.
Local Benchmarks
Data: 100+ local platforms per country, benchmarked against what people in that market actually use.
Value: tells you where a real local identity should show up — and where to put local marketing budget.
Six answers.
Six signals, priced in euros.
Verification & Health Signals
You optimize: screen out unsafe leads before a campaign send or a manual review. Money: €1/check to screen 10,000 applicants is €10,000 — a fraction of what one missed fraud ring or a spam-flagged campaign costs (see Case 1 & 3 ahead).
Interests
You optimize: the offer you lead with, instead of one blanket pitch. Money: a matched offer converts measurably higher, so the same campaign spend buys more customers.
Digital Footprint
Alternative data for thin-file customers: a bureau can't score someone with no credit history — a young applicant, a new arrival. A digital footprint (300+ OSINT signals, device intelligence, local signals) approves legitimate customers they'd miss entirely (see Case 1 ahead).
AI Customer Persona
You optimize: Google/Meta targeting and your own segments, same-day instead of a multi-day build. Money: lower cost per campaign launched, less spend wasted finding the audience.
Human or Bot
Fraud prevention, not decisions: an email created 3 days ago with zero social presence is exactly what a synthetic identity looks like — we flag it, your model or team decides. Legitimate agents get a fast path instead of a lost sale (see Case 3 ahead).
Local Benchmarks
You optimize: where regional acquisition budget goes. Money: the same logic that protects CAC in Case 2 ahead.
A context layer, not a decision-maker. We feed your credit model, we never replace it — signals, not verdicts. And we connect what marketing is chasing at the front door with what risk and underwriting expect in the back office, so the same signal drives both.
10,000 applications/month,
Southern European market entrant
No credit bureau access, thin-file borrowers, fewer than 50 internal variables. SocialScore added 300+ external variables at the application step.
Why: the same Digital Footprint and Verification & Health Signals from the Signals slide turn a bureau-blind thin-file applicant into one the lender's own model can see and approve — Digital Footprint signal → more approvable applicants → more loan volume → the €53.9M+ above. Social Signal as a digital-stability proxy, LinkedIn job/employer as an income proxy, Fraud Signal as a synthetic-identity indicator — informational only, the lender's system makes every decision. Approval rose because good applicants became visible, not because underwriting loosened: default fell at the same time. The lender's own rules: a Fraud Signal above 50 or a disposable phone routes to manual review; a clean match to their "Stable Professional" segment fast-tracks approval.
8,000 digital account applications/month,
checking & savings
Pre-verifying identity via digital footprint match lets the bank skip redundant document requests for confirmed applicants — the single biggest driver of UYC abandonment.
Why, and where the data comes from: the signals are the applicant's own public digital footprint from the Signals slide — verified email/phone reachability, platform presence, breach exposure — matched against the segment the bank's marketing team already defined as "good fit." Digital Footprint signal → skip the redundant document request → fewer abandoned applications → the €216,000/mo above. In Europe, 68% of consumers have abandoned a financial-services application in the past year (Signicat, 2025), and banks worldwide report losing clients to slow onboarding (Fenergo). The same match doubles as a marketing input: a verified persona of who actually converts, so the next acquisition campaign can target look-alike prospects at a lower cost per new customer. Cost basis: 8,000 checks/month × €1.
25,000 instant-decision applications/month,
€4M/month in BNPL volume
Instant approval leaves almost no window to catch fraud — Fraud Signal, Local Benchmarks and the Human-or-Bot check run inside the same sub-5-second decision.
Why, and where the data comes from: the signal is public digital-footprint depth from the Signals slide — how long an identity has existed across local platforms, not a credit file. Local Benchmarks signal → catches the near-zero footprint a fabricated identity always has → fewer fraud payouts → the €61,200/mo above. BNPL's near-instant approval window is exactly what synthetic-identity and account-takeover fraud is built to exploit (DataVisor, ICBA, 2026). It's an indicator the provider's own fraud team weighs, not an automated block. Cost basis: ~25,000 checks/month, volume pricing applied above the 10,000/month threshold. Fraud benchmark: BNPL charge-off rates run ~1.8–2% of GMV industry-wide (Chargeflow, 2026).
Same 4,000 customers.
Same cross-sell budget. Very different outcome.
A worked example, from the lender's side — what changes when SocialScore sits between your customer book and your next-product offer.
Why: the 140 vs. 310 accepted offers are the same €6,000 campaign budget — the only thing that changed is knowing who's actually ready for the next product, from the €4,000 identification pass. That's the growth lever. Separately: dormant accounts get re-checked automatically, so risk that drifted since onboarding gets caught before it compounds — that's the retention and risk lever, and it runs every month after this one without new setup.
Same product. Same call.
Three completely different applicants.
This is what the signals actually look like on real applicants — and what each one means for a financial product decision.
Over 50% of online traffic is bots today — and some are starting to apply and buy. Nobody can tell who's really behind the screen, or what they want, not even your own chatbot. We give your models that context — and with it, the revenue that depends on it.
The Customer Intelligence Layer
vs. what you have today
| Comparison | SocialScore | Credit Bureau | In-House Build | KYC/AML Vendor |
| Cost to start | €0 | Per-pull fees, ongoing | €500K+ | €300K+ |
| Sees thin-file / no-file applicants | Yes, 300+ signals | No — credit-active only | Depends on build | Not its job |
| Verifies AND Signals AND Predicts | Yes, one call | Scores only | Yes, if you build it | Verifies only |
| Time to first result | 1 day | Immediate, limited | 6+ months | 4+ months |
| Prediction basis | Real-time data | Historical credit file | History only | History only |
What's actually broken about knowing the applicant today
Built to
improve what you run, not to replace it
SocialScore was designed as an enrichment layer from day one — one API call, live in about 10 minutes, gives you enterprise-grade predictive software without disturbing your core banking system, LOS, or KYC/AML stack.
Your own Data Science team, on call
Every account includes access to our data science team — for signal interpretation, custom signal logic, and model integration.
Customized to your exact stack
Proprietary LOS or core banking system? We build a custom module to your specification — same enrichment, embedded into your flow.
AI Tools
Chatbots, credit assistants, pricing logic — connected via MCP, so your AI gets the same context a human underwriter would.
CRM / Risk Engine
Any platform with a webhook or an import can pull SocialScore signals through the API — no vendor lock-in.
Handling identity and risk data means
compliance isn't optional
We process signals on your instructions, for the purposes you set. We provide signals, never scores or verdicts — we don't decide what happens to the applicant.
You decide why the data is processed and what happens with the result. Every actual decision is yours.
The person the signals describe. Their GDPR rights — access, erasure, objection — route through you as controller.
What We Process For
Two purposes only: identification (verifying who someone is) and marketing (understanding what they might want) — never anything else without your instruction.
Where Your Data Lives
Pass-Through (default): we return the signals, keep no copy. Storage Mode (opt-in): we retain the profile for you to re-query, deletable on request — both covered by the same DPA, signed before production use.
SocialScore commits to signal transparency and strict compliance: we operate purely to help you with identification and marketing, we never output scores or verdicts, only signals for your own systems to weigh. Because the Controller — your business — makes every actual decision, SocialScore sits outside GDPR Art. 22's automated-decision rules and outside the EU AI Act's high-risk (Annex III) category — both apply to the system that decides, not the one that informs it.
GDPR — Standard Tier
Public digital-footprint data, never Art. 9 special-category data — the standard risk tier, not the enhanced one.
ISO/IEC 27001:2022
Our security program is aligned to this standard, so you don't have to audit our infrastructure yourself.
EU-Based Entity
DSNTech LTD, Sofia, Bulgaria — subject to EU law, not a third-country transfer.
Never Resold
Your enriched data is yours — never sold on to advertisers or other clients.
Get an expert read on your applicants,
not just another data feed — free.
Bring your own applicant list. Our team enriches it live, in front of you, walks through what it means for approval, fraud, and marketing, and shows you exactly who's behind those applications today.
Already live with fintech companies across Europe — lenders, banks and payment providers using SocialScore today to optimize approval, fraud, and growth.
office@socialscore.io · dimitar@socialscore.io · DSNTech LTD, Mladost 2, Sofia, Bulgaria