Pometry X Syntheticr
Transforming Financial Crime Analytics
In the fast-evolving landscape of financial crime detection, organizations must leverage cutting-edge technology to stay ahead of emerging threats.
Pometry, a leader in real-time graph analytics, developed a sophisticated financial crime analytics solution designed to uncover hidden patterns in transaction data.
This innovative solution was built and demonstrated using Syntheticr 1.0, which enabled Pometry to showcase the effectiveness of their platform without any of the drawbacks of real-world data.
Traditional approaches to financial crime detection rely on historical data, which can be incomplete or biased. Also, the sensitivity of real customer data raises significant challenges and negatively impacts innovation.
Pometry recognized the need for a solution that could analyse vast amounts of transaction data in real time while ensuring compliance with data protection regulations.
Syntheticr 1.0 provided the ideal foundation for Pometry's financial crime analytics solution.
By offering high-quality synthetic data that mirrored real-world banking transactions, Pometry were able to:
By leveraging Syntheticr 1.0's diverse synthetic dataset, Pometry was able to create a sophisticated analytics framework capable of identifying suspicious activity in real-time.
Develop a Robust Analytics Framework
Using fully synthetic data allowed Pometry to develop their solution without the risks associated with using personal information, ensuring compliance with GDPR and other privacy regulations.
Ensure Privacy and Compliance
With the ability to test and validate their solution against realistic scenarios, Pometry effectively demonstrated the power of their technology to stakeholders and potential customers.
Demonstrate Effectiveness
Pometry’s financial crime analytics platform harnesses the power of their proprietary temporal graph database to visualize and analyze complex relationships within financial transaction data.
The integration of Syntheticr 1.0 data enabled several key features:
The synthetic dataset allowed Pometry to model various transaction scenarios, leading to the development of advanced algorithms capable of identifying unusual transaction patterns indicative of financial crime.
Dynamic Pattern Recognition
The platform’s real-time capabilities, powered by Syntheticr 1.0, enable instant detection and response to suspicious activities, allowing organizations to act swiftly in mitigating risks.
Immediate Insights
By using synthetic data to test scenarios, Pometry was able to rapidly refine its algorithms to ensure the platform could be optimised to accurately detect complex financial crime activity.
Scenario Testing and Validation
Pometry successfully piloted its financial crime analytics solution showcasing the platform's capabilities. Using Syntheticr 1.0, the team demonstrated how their temporal graph database could be used to uncover networks of criminal activity.
In one notable demonstration, Pometry analyzed a series of simulated transactions and was able to identify suspicious connections between accounts that would otherwise have gone unnoticed.
This illustrated the power of their detection capabilities and established their temporal graph database as a key innovation in the space.