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DataSchutz
Securing Payment Infrastructures
CELT-UV · Cryptographic Ephemeral Limit Token — Upgraded Version

Fraud is caught after the money is already gone.
We stop it mid-transaction.

DataSchutz provides pre-emptive interdiction infrastructure for payment rails — evaluating five live risk vectors inside the authorization window and killing fraudulent transactions before capital leaves the originating bank.

See the validation data → How CELT-UV works
LIVE TRANSACTION WINDOW · 24–30ms decision CELT-UV ACTIVE
TERMINATED · geo mismatch
Authorized (5-vector match) Killed mid-stream (anomaly) 10,000+ tx simulated across 3 datasets
SIMULATED LIVE FEED 0 tx evaluated this session
The problem

Static rule engines catch fraud after the damage is already done.

Under RBI's Digital Fraud Compensation and Risk-Based Authentication guidelines, the burden of proof — and the cost of every dispute — now sits with the bank. Legacy systems weren't built for that shift.

Post-authorization review

Fraud is found after the money moves

Rule-based engines and lookup systems flag fraud once a transaction has already been triggered — forcing banks into costly disputes and reactive investigations instead of prevention.

Regulatory exposure

The liability now sits with the bank

RBI's new compensation and risk-based authentication guidelines shift the burden of proof onto financial institutions, turning every undetected fraud event into a direct financial and reputational liability.

Blind to execution threats

Modern attacks happen mid-transaction

Terminal manipulation and transaction rerouting occur while a payment is in flight. Legacy infrastructure has no visibility into the execution window — it only sees the outcome.

Trust erosion

Every incident costs a customer

Each unresolved fraud dispute compounds churn risk. Banks need a way to stop the loss before it happens, not a faster way to process the claim afterward.

The solution

Pre-emptive data evaluation, inside the authorization window.

Instead of reviewing fraud after authorization, CELT-UV intercepts the payment at the gateway and evaluates five live data vectors — App Behavior, Device, Location, Transaction Pattern, and Merchant Score — while the transaction is still in motion.

01 / PRE-EMPTION

Kill before capital moves

When an anomaly is identified mid-stream, our integrated CELT-UV architecture terminates the transaction in real time — before funds ever leave the originating bank.

02 / ZERO FRICTION

Invisible to legitimate users

Risk decisions are delivered in milliseconds, so genuine transactions clear exactly as they would today — no added steps, no added friction.

03 / LIABILITY SHIELD

Loss prevented at the source

By stopping fraud before authorization, CELT-UV shields financial institutions from mounting compensation claims under RBI's new liability framework.

How CELT-UV works

A three-stage interdiction pipeline, running inside the payment gateway.

ITokenize

Wrap the payment in a one-time CELT-UV

The engine wraps the payment in a unique, one-time Cryptographic Ephemeral Limit Token — Upgraded Version. Dynamic spending limits and authorization rules are baked directly into the token's payload — not bolted on afterward.

CELT_UV_PAYLOAD { token: ephemeral, single-use spend_limit: dynamic auth_rules: bound-to-payload }
IIIngest

Five live vectors, evaluated pre-authorization

The API instantly ingests five live signals before the transaction is authorized — giving the model a real-time picture of both the user and the merchant, not just the transaction amount.

App Behavior Usage pattern match
Device Integrity + ID check
Location Geo consistency
Txn Pattern Spend behavior
Merchant Risk score
IIIDecide

Kill mid-transaction, or authorize

If any anomaly is detected — geo mismatch, inconsistent app behavior, an unrecognized device ID, an abnormal spending pattern, or a low merchant risk score — the ML model kills the transaction midway. Otherwise, it authorizes instantly.

VISUAL WALKTHROUGH · one transaction, start to finish DECISION LOGIC
Payment Initiated STAGE I Payment wrapped in one-time CELT-UV App Behavior Device Integrity Geo- Location Txn Pattern Merchant Score STAGE II ML model scores all 5 vectors STAGE III Anomaly detected? NO Authorize instantly Funds settle normally zero added friction YES Kill transaction mid-stream Funds blocked at source bank never exposed
Safe path — no anomaly, cleared instantly Kill path — anomaly caught pre-authorization
Validation & evidence

Local simulations across three independent datasets.

CELT-UV V1 is functional and locally calibrated. Results below are from local simulation, not production deployment — more data is needed to raise accuracy further.

Kaggle · IBM Dataset

Enterprise transaction data

Precision0%
Recall0%
False Positive Rate0%
Latency0 ms
n = 10,000 transactions
Kaggle · Synthetic Dataset

High-concurrency fraud simulation

Precision0%
Recall0%
False Positive Rate0%
Latency0 ms
n = 14,000 transactions
IIT-Hyderabad Hackathon

Independent hackathon dataset

Accuracy0%
Recall0%
False Positive Rate0%
Latency0 ms
n = 9,082 transactions
DPDP & GDPR alignment

Strict purpose limitation — telemetry, not identity.

Data ingested is strictly confined to active transaction risk scoring and security validation. We capture only the vectors required to run and optimize CELT-UV, and DART (under development).

What we never touch

Our architecture does not store or process core personal identity data — no names, national IDs, or bank account numbers. Models evaluate purely contextual, device, and behavioral risk intelligence, keeping the privacy footprint to an absolute minimum.

What we ingest

  • User app behavior
  • Device integrity signals
  • Location
  • Merchant / POS score
  • Transaction patterns

Training pipelines process telemetry patterns, architecturally isolated from persistent PII — aligned to both India's DPDP framework and GDPR.

Traction & IP

From provisional filing to functioning engine.

Complete

Incorporation

Company incorporated

Complete

DPIIT Recognition

Recognized under India's Startup India initiative

Complete

UoH TIDE Programme

Accepted into University of Hyderabad's TIDE cohort

Filed

CELT-UV — provisional patent

Application No. 202641016177

Filed

DART — provisional patent

Dynamic Authorization and Risk Tokenization · Application No. 202641045724

In development

Wire transfer security infrastructure

Patent filing targeted for Q4 2026

Calibrating

CELT-UV V1

Functional and locally calibrated — scaling data volume to increase accuracy

Get in touch

Let's stop fraud before the money moves.

We're working with partner banks to pilot CELT-UV against live gateway traffic. If you're evaluating pre-authorization risk infrastructure, we'd like to talk.

Request a technical briefing → Download one-pager