By leveraging domain specific language (DSL) reconciliation definitions, the solution provides users with the agility and flexibility needed when configuring complex reconciliation processes. Additionally, the Hadoop® MapReduce parallelism guarantees high performance even when big data is in play and it is dealing with large amounts of information.
This solution can process large amounts of data in multiple formats and can be driven by specific market trading times or users can input custom schedules in the platform. Data feeding in to the solution via different protocols, such as FTP, MQ, FIX, SWIFT, etc., and in different formats – including but not limited to text, CSV, excel, relational databases, XML – is transformed into a core data layer ready for mapping and reconciliation.
Critical one-to-one, one-to-many or many-to-many matching algorithms ranging from simple field equality matching (if required, within specified tolerance limits) to more complex algorithms involving the handling of substrings within text fields provide the necessary edge to the process. This flexible offering can be deployed via NRI’s secure cloud system and is also available for on premise installations, providing financial institutions the option to choose the model which best suits their business infrastructure.
- Scheduled or user-triggered Apache™Hadoop® and Cloudera Hadoop®-based -MapReduce processes driven by DSL-based reconciliation definitions use the Hadoop
- Distributed File System (HDFS®).
Enables the users to concentrate on breaks and provides them with the ability to assign breaks for actions to -groups and individuals.
Provides users with real time, customizable access to a vast range of reconciliation information ‐ including feeds, counters and chart components.
Open source technology powered by Spring™framework-based infrastructure, Hibernate / JPA-based persistence and Apache Camel™ for data process.