Engineering a Secure AML Platform for Cross-Bank Fraud Detection

Over view

Consilient works in the strictly regulated and security-oriented field of anti-money laundering (AML) space. They have recently launched a new platform capable of spotting fraudulent transactions across different banks. Initially, the company was training machine learning models on sensitive banking data and then returning the models so that none of the banking transaction data left the environment.
As a platform engineering consultancy, we partnered with the company to create and implement software packages that integrate smoothly with the bank’s systems. We also developed the first cloud computing platform to handle and combine the models from each bank. This has been game-changing for the company, as is its ability to amplify its efficiency in cross-bank fraud detection.

 

Overcoming Development Hurdles

Banks have highly restrictive environments. Since different banks provide different quality of data, it can be tough to make sure the model is improving consistently, and building a fraud detection system that spans multiple banks in the AML sector presents a lot of tough challenges. Our team had to be extra vigilant with any sensitive information. Additionally, the inconsistency in data quality among different banks made it even more difficult to ensure that our models would perform well consistently. Since our platform needed to combine models without having direct access to the raw data, we had to figure out how to train these models using representative data while still protecting privacy. Another big challenge was developing software that could work across various banking systems. Standard container and package management techniques were not suitable because banks had to inspect the software and scan the contents, and Docker was not commonly used in their environments.

This meant we had to think outside the box to create solutions that did not depend on the bank’s current software or infrastructure assumptions while still being compatible across different platforms.

A New Route to Success

Initially, the platform was designed with Apache Spark. This choice was found to be unsuitable because it was too reliant on the banking infrastructure during deployment time. The complexity of Spark made it challenging to create
and deploy pipelines in a consistent way across different operating systems and data analytics environments.
We decided to restructure the platform using the Hamilton pipeline engine and Polars in order to tackle these problems. This allowed for more efficient data processing and enabled the use of Python for writing features, avoiding the complicated DBT-based SQL scripts. The code was also compiled into a C package to enhance security and portability, which removed the need for a Python environment on the customer’s end and streamlined the deployment process.

 

This new solution could operate on any modern operating system and improved the platform’s flexibility without sacrificing security.

Redefining Financial Crime Detection

The new innovative federated learning technology allows the platform to now manage data sets that go beyond traditional memory limits. This advancement allows it to meet the intricate data needs of today’s financial crime detection.
The platform now offers fully testable features, which enhance the accuracy and dependability of fraud detection. It can be easily installed on any modern operating system without requiring prior infrastructure, and it is now compatible with different banking settings. This adaptability allows Consilient to quickly test, iterate, and roll out new features, speeding up innovation in the fight against financial crime.

What began as a set of sample codes and ideas has transformed into a fully functional product ready to meet the challenges of fraud detection in the AML field.

 

Achieving a Limitless Future

Concept to Cloud helped Consilient take financial crime detection to the next level by changing the way secure and scalable platforms are created. With their advanced cloud architecture, we helped Consilient to safely manage and integrate machine learning models from various banks. The new platform has allowed them to maintain privacy while still delivering high performance. By smoothly connecting the platform with different banking systems, Consilient’s platform is now ready for a bright future ahead with the highest quality security measures in place.

 

At Concept to Cloud, we harness the expertise of seasoned, ex-NASA engineers to transform early-stage startups into venture-ready companies. We collaborate closely with emerging businesses to rapidly develop and deploy functional prototypes and MVPs, helping them secure funding and achieve early market validation. Our tailored cloud solutions, combined with our adaptable engagement models, ensure that your technological foundation is robust, responsive, and perfectly aligned with your startup’s evolving needs. Discover how our strategic approach can accelerate your path to market—partner with us to turn your concept into reality.

If your company could use our expert knowledge in deploying and scaling systems, then book an introductory call and find out how Spicule can help.

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