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CASE STUDY

Real-Time Risk Data Platform for a European Fintech

Confidential — European Fintech — Financial Services

8h → 4s

Data latency reduction

+28%

Fraud detection improvement

60% → 9%

Engineering time on maintenance

THE CHALLENGE

Challenge

A fast-growing European payments company was running risk models on overnight batch data. By the time fraud signals were acted on, transactions were already settled. The existing pipeline was a tangle of bespoke Python scripts with no monitoring, no schema validation, and a 3-day SLA for any change. The data team spent 60% of their time on maintenance.

OUR APPROACH

Approach

We replaced the batch pipeline with a Kafka-based streaming architecture feeding a Delta Lake medallion platform. Bronze layer ingests raw events with full schema preservation. Silver applies deduplication, type coercion, and quarantine logic. Gold exposes risk-ready aggregates to the ML models. We built a data quality framework with automated alerting and migrated the team to dbt for transformation logic.

THE RESULTS

Results

Risk models now run on data that is seconds old instead of hours. The fraud detection rate improved by 28% within three months of go-live. Engineering time spent on pipeline maintenance dropped from 60% to under 10%. The platform now supports five downstream ML models that did not exist before the migration.

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