Financial Institution Reduces Fraud Risk using AI built on AIStor Data Lakehouse

1. Executive Summary

A leading national payment and financial infrastructure provider transformed its analytics and fraud detection capabilities by migrating from a legacy Hadoop system to a modern data lakehouse powered by MinIO AIStor and Trino on Kubernetes. The shift delivered a 65% reduction in fraud model runtime, a fivefold increase in workload capacity, and significant cost savings by replacing legacy licenses with open-source solutions.

Key Benefits

• Cloud agility on-prem: AIStor’s S3-compatible storage provided elastic scalability within the organization’s private infrastructure.
• Massive performance gains: Fraud detection jobs dropped from 20 hours to just 6–7 hours, even as data volumes doubled.
• Open-source flexibility: By replacing proprietary Hadoop software with AIStor and an open-source stack including Trino, Spark, and Flink, the organization reduced costs and improved innovation velocity.

2. The Environment

This organization operates the digital backbone of a major country’s financial ecosystem, handling billions of real-time transactions daily. As transaction data volumes grew to petabyte scale, the company faced increasing pressure to modernize its data lake infrastructure and support AI-driven fraud detection at scale.

3. Challenges

The legacy Hadoop environment tightly coupled compute and storage, creating scalability bottlenecks. Fraud detection models required up to 20 hours to process 700TB of data, leaving teams constrained by slow query performance, limited concurrency, and rising infrastructure costs.

4. The Vision

The organization sought to build a cloud-native, open-source data lakehouse that would decouple compute from storage, enabling faster analytics, self-service data access, and cost-efficient scalability. The goal was to empower engineers and analysts to run more models, faster, while maintaining on-premises compliance for sensitive financial data.

5. The Solution: MinIO AIStor 

The institution implemented AIStor as its high-performance object store, paired with Trino for distributed query processing on Kubernetes. This modern architecture separated compute from storage and supported multi-cluster deployments for various workloads such as analytics, production, and dashboards.

6. Results & Outcomes

The migration delivered measurable business and technical improvements:

• 65% faster model runtimes for fraud detection
• 5× increase in total query throughput
• Scalability to 1.5PB of data with over 6,000 daily queries

7. Unexpected Wins

Beyond performance and cost improvements, the organization found that teams could innovate faster. Multi-cluster isolation reduced query contention, and AIStor’s simplicity allowed for rapid deployment and scaling of new data-driven use cases.