Customer story

Norden Bank

A fraud model that was trained without one real customer.

Norden's risk-analytics team rebuilt its card-fraud pipeline on a Doppelset twin. The model performs within 0.6 AUC of production — and any vendor, intern or auditor can now look at the data.

At a glance

Industry
Banking
HQ
Stockholm, Sweden
Size
8,200 employees · €1.4 T AUM
Stack
Doppelset Lab (dedicated tenant), Snowflake · dbt

10×

faster vendor onboarding

0.6 AUC

gap vs. production model

€2.1 M

annual saving in DSAR + audit cost

The challenge

Norden's fraud-detection model relied on a constantly-refreshed view of card transactions — millions of rows a day, each carrying PAN-hashed identifiers, merchant metadata, geolocation, and outcome labels. Every external partner who needed to look at the data — model vendors, the FCA reviewer, the consultancy auditing the model — required a fresh data-sharing agreement that took three months to land.

The team's internal vendors also struggled. Junior analysts couldn't get hands-on training data; the production environment was so locked down that no one could prototype outside a remote VDI session.

The solution

Norden deployed Doppelset Lab on a dedicated tenant in eu-north-1, attached to their card-network data warehouse via a read-only replica. A relational twin was trained over four tables — accounts, transactions, merchants, disputes — preserving FK cardinalities and the 0.4% fraud base rate.

Every night, the twin re-trains on the previous day's data and emits 6 hours later a one-week synthetic window. Vendors, analysts, and the regulator query this synthetic window through Snowflake — using the same SQL as production, just pointed at a different schema.

When the team needs balanced data for training a new fraud model, they call twin.sample(rows=100_000_000, balance={'is_fraud': 0.5}) and get a perfectly balanced corpus on demand, with a signed report attached.

Our fraud team finally has a shared dataset everyone — analysts, vendors, the regulator — is allowed to look at. That alone paid for the platform.
Joaquín Salas, VP Risk Analytics, Norden Bank

The results

Vendor onboarding fell from 92 days to 9

External vendors now receive a synthetic snapshot and the signed receipt under NDA on day one. No more three-month DSA cycles for proof-of-concept work.

A fraud model that holds up

The fraud model retrained on the synthetic dataset, then tested on a held-out week of real data, came within 0.6 AUC of the production baseline. Crucially it retained the rare-fraud-typology long tail.

An audit that didn't bite

Norden handed the FCA reviewer the signed Doppelset receipts alongside the model card. Review wrapped in eight days — Norden's fastest ever.

Internal experiments unlocked

Junior analysts now prototype fraud-detection ideas in regular notebooks against the synthetic warehouse. Number of new fraud signals tested per quarter jumped from 4 to 27.

What's running

Doppelset Lab (dedicated tenant)Snowflake · dbtAirflowEntra ID SSOAWS eu-north-1

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