Fraud detection and monitoring software

• Lack of data. Efficient monitoring applies risk analytics and detection of high-threat activities in real-time. In order to generate actionable data, we had to create innovative analytics architecture and integrate it with various Artificial Intelligence and Business Intelligence tools.
• Data collection and validation. Once we gathered a substantial amount of the relevant data, it became crucial to apply proper structure and modeling. Indexing and validating the data streamlined the future incoming data processing.
• Automatically generated reporting. The client needed three types of reports: General view, Over Time view, and Contextual view in order to compare current items with the relevant data on a fly.
• Risk engine settings. Our experts designed and developed the following new features: Employee spend analysis, HCX research, and Vendor spend analysis. These functionalities were implemented on multiple platforms and devices.
• As a global company, our client required the ability to download reports in multiple languages. We enabled the system to generate reports in the most common 14 languages on the planet.

Our team of experts participated in the wholesale design and development of the system and the specific features and product improvements. All the components are horizontally scalable and fault-tolerant, thanks to the data replication and synchronization to the core product. The following companies use the software that we developed:

• We outlined a clear design & development roadmap, set multiple sprint goals, and mapped & documented future development plans to assess product development timelines better.
• Competitive analysis and market research were performed using our proprietary AI research tool Marquètte. 
• We conducted A/B testing of the Tableau and Metabase to ensure the client has actual valid data available. 
• After Investigation and comparison of open-source Business Intelligence tools, we opted for Metabase and created SQL-based inquiries. For the client’s convenience, we embedded Metabase Dashboards into the application. We trained our client on how to use the Metabase efficiently for a more seamless internal employee workflow. 
• An always-on architecture and a single-source data warehouse. Our team of engineers opted for working with PostgreSQL, a database type, and AWS Redshift, a database host. 
• In-depth reporting with ad-hoc drill-down to the data level: 3 types of reports: General view, Over Time view, and Contextual view. We have also built a 24/7 digital assistant to explain the risks raised to the end-users.

Our primary focus on data management and analytics has enabled our client to understand their customers’ needs and provide more useful services. Following our extensive analytics approach, which included a precise analysis of KPIs for each feature, our client could make efficient decisions on further improvements. This has resulted in the embedding of machine learning-based solutions that give out important insights and efficient analysis.