AML False Positives Cut 75%
Consilient · From Weeks to Hours per Bank Deployment
How we replatformed a financial-crime detection pipeline to unlock a 75% false-positive reduction and same-day deployment into every new banking environment.
Consilient operates at the forefront of financial crime prevention, using federated learning to help banks detect money laundering and terrorist financing without compromising data privacy. Their technology promised to revolutionize AML compliance, reducing false positives by 75% while improving high-risk detection by 300%.
But there was a problem: their Apache Spark-based pipeline couldn't scale to meet market demand. Each bank's unique environment required weeks of custom configuration and bespoke builds. Concept To Cloud partnered with Consilient to rebuild their entire data processing pipeline, transforming their approach from custom implementations to a unified product that could deploy anywhere in under a day.
The Deployment Nightmare
Weeks-Long Deployments
Every banking environment was unique. What worked for one institution failed in another. Deployment cycles stretched to weeks as engineers wrestled with environment-specific configurations, operating system variations, and infrastructure dependencies.
Bespoke Build Hell
Apache Spark required independent builds for different operating systems and backend configurations. Each banking environment became a custom project, impossible to scale, expensive to maintain, and frustrating for everyone involved.
Black Box Banking
Banking analytics environments operated as secure black boxes, Consilient had no direct access. Debugging deployment issues became a game of telephone, with each iteration taking days. The heavily regulated environment demanded a solution that worked perfectly the first time.
The Polars Revolution
Over three months, a focused team of four engineers completely reimagined Consilient's data processing pipeline, replacing Apache Spark's complexity with Polars' elegance and portability.
Single Binary Deployment
Polars enabled compilation to a single, self-contained binary. No runtime dependencies. No JVM requirements. No distribution variations. Just one file that ran identically everywhere, from cutting-edge Linux clusters to legacy Windows servers.
Hamilton Pipeline Engine
We integrated the Hamilton pipeline engine to bring clarity and maintainability to complex data workflows. What had been opaque Spark transformations became transparent, testable, and easily modified Python functions.
Universal Compatibility
The new pipeline ran on any hardware with bare metal access. From the couple of analytics environments Spark supported to literally anywhere a bank could provision compute, Windows, Linux, any configuration.
Python Ecosystem Unlocked
Breaking free from Spark's constraints meant access to the entire Python ecosystem. Any library, any tool, if Python supported it, Consilient could now use it for feature engineering and model development.
Technology Transformation
- • JVM dependencies
- • Environment-specific builds
- • Limited library support
- • Week-long deployments
- • Single binary deployment
- • Universal compatibility
- • Full Python ecosystem
- • Same-day deployments
The Migration Journey
Three months. Four engineers. Complete transformation.
Pipeline Analysis & Planning
We started by deeply understanding Consilient's existing Spark pipelines, identifying pain points, mapping data flows, and designing a Polars-based architecture that could match or exceed Spark's capabilities while eliminating deployment complexity.
Hamilton Integration
The Hamilton pipeline engine became the backbone of the new system. Instead of Spark's black-box transformations, we created explicit, testable Python functions that clearly expressed business logic. Data scientists could now understand and modify pipelines without Spark expertise.
Polars Performance Optimization
Polars' query engine delivered impressive performance out of the box, but we went further, optimizing data types, leveraging lazy evaluation, and structuring queries for maximum efficiency. The result matched Spark's speed while using a fraction of the resources.
Binary Compilation & Packaging
We engineered a build process that compiled the entire pipeline, Python code, Polars runtime, all dependencies, into a single executable binary. Banks could now deploy Consilient's technology by copying one file. No installation. No configuration. It just worked.
Transformative Results
From bespoke to product: What had been custom engineering projects became a unified product. One codebase. One binary. Infinite deployment targets.
Faster customer onboarding: Banks that once required weeks of engineering effort could now be onboarded in days. Fewer people. Less time. More revenue.
Feature velocity unlocked: Access to the entire Python ecosystem meant rapid feature development. Libraries that would have taken months to integrate with Spark now worked out of the box.
Operational simplicity: No more environment-specific debugging. No more custom builds. Support tickets dropped as deployments simply worked the first time.
Market expansion enabled: Smaller banks with less sophisticated infrastructure could now adopt Consilient's technology. The addressable market multiplied.
From Custom Chaos to Product Excellence
Consilient's transformation shows the power of choosing the right technology for the problem at hand. Apache Spark is an incredible tool, for the right use cases. But for Consilient's needs, it was overkill that created deployment nightmares.
By migrating to Polars and Hamilton, we didn't just solve technical problems. We transformed Consilient's business model. They went from a services company doing custom integrations to a product company with a unified offering that could deploy anywhere.
Three months. Four engineers. One decision that changed everything. That's the impact of partnering with engineers who understand not just technology, but business outcomes, and the demands of regulated, anti-money-laundering work.
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