arrow
Back to Resources
Solution Brief

Why Fraud and Risk Analytics Fail at Scale — and How to Fix the Data Layer

About This Resource
Simple maroon user profile icon with circular head and curved shoulders.
Who This is For:

Financial services data and technology leaders — including fraud operations, risk analytics, and compliance engineering teams — responsible for scaling AI-driven detection while managing the cost and complexity of fragmented transaction data infrastructure.

Key Takeaways
Blue check mark inside a light blue circle.

Fraud and risk analytics fail not because of weak models, but because fragmented data architectures — Hadoop clusters, siloed copies, and bolt-on object storage — prevent analytics and AI from operating against unified, complete transaction data.

Blue check mark inside a light blue circle.

AIStor's native WORM-compliant immutable storage and active-active multi-site replication address SEC 17a-4, GDPR, and BSA compliance requirements without custom tooling or additional governance layers.

Blue check mark inside a light blue circle.

NPCI migrated from Hadoop/HDFS to AIStor and Trino, achieving 65% faster fraud model runtime and 5x query throughput while scaling to 1.5 PB with 6,000+ daily queries.

Transaction data now arrives from more sources, at higher velocity, than the architectures built to analyze it can handle. The result is fragmented infrastructure that grows more expensive and more brittle with every new initiative, while fraud and risk models underperform because they never operate against a complete, unified data set. This whitepaper diagnoses three specific failure modes: Hadoop's batch-first design and 3x replication overhead; the real-time versus historical tension that forces teams to choose between recency and depth; and bolt-on object storage that renders archived transaction data invisible to analytics workflows. The solution is a consolidated data foundation built on MinIO AIStor, serving fraud detection, AML, risk modeling, regulatory reporting, and cybersecurity threat detection from a single platform. Customer results include NPCI scaling to 1.5 PB with 6,000+ daily queries and Nomura recovering 4+ hours of SLA headroom with 50%+ TCO reduction.

Article on why fraud and risk analytics fail at scale and the need to fix data infrastructure.
View Resource
Icon of a document with a folded corner and horizontal lines representing text.

Related Resources

Solution Brief
F5 Solution Overview Scaling AI Data Ingestion With MinIO Red Hat And F5
How F5 BIG-IP, Red Hat OpenShift, and MinIO AIStor work together to scale AI training data ingestion securely at exabyte volumes.
View Resource Details
arrow
Webinar
Beyond Hadoop: Supercharge Your Analytics with Dremio and MinIO AIStor
Discover how Dremio and AIStor together can supercharge your analytics, reduce costs, and modernize your data architecture—on-premises or in the cloud.
View Resource Details
arrow
Webinar
From On-Premises to Insight: Zero-Copy Analytics with Databricks and MinIO AIStor
Learn how AIStor Table Sharing gives Databricks live, governed query access to on-premises Iceberg and Delta tables, with no pipelines, data copies, or vendor lock-in.
View Resource Details
arrow
Contact Us275 Shoreline Dr, Ste 100, Redwood City, CA 94065, United States