With our client from Miami, USA, we have moved his anti-money laundering application functionalities from enterprise to the new lightweight version dedicated to smaller and medium-sized businesses. Moving the whole application from PHP to Python allowed for increased responsiveness, stability, and readiness for future iterations. Lightweight application - Global Radar Check was based on name search and identifying searched personas. Application provided multiple channels through which such person could be identified like social media mentions, press releases, global background check, or association to dangerous entities. Our cooperation resulted in implementing new features to Global Radar Check like sanctions world map, online accounts verification, company network builder, and significantly improved UX experience. Its continued development is present today as Global Radar grows and becomes one of the most significant key players in the ALM market.
Global RADAR is an all-in-one regulatory compliance & anti-money laundering software platform from USA, Miami, providing risk management solutions to businesses since 2007.
The aim was to deliver a backend foundation for the lightweight application based on its big enterprise brother. The transformation from PHP to Python required rewriting big chunks of code. After implementing all functionalities, we made sure all external integrations worked seamlessly and did not stutter. Global Radar Check lastly was covered with modern UX to simplify its usage for the employees. The big challenge was the time in which we were supposed to deliver the application. We were obliged to deliver working software in three months. It led to the decision to start with an MVP and consequently deliver the project's following parts.
The lightweight app, which we had delivered is the smaller version of the big enterprise version. The development of Global Radar Check had to be precise and help alleviate the workload of the enterprise version. We chose Python for better stability, an easier path in further development, and cost-effectiveness. During development, the requests were firstly processed and sent further to the API. As it reserved space after the return, it clogged the available channels. We had to create as fast as possible RestAPI for communication with the main enterprise app.