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Research Framework for Electronic Fund Transfer(EFT) – Switch Processor Interface (PI)
- Developed a machine learning based research Framework for EFT- Switch Processor interface and card authorization using TAL and TACL, achieving 90% analytic and automated testing support for online transaction processing.
- Tableau-based dashboard with a capability to visualize the end to end transaction flow along with issuer and acquirer transaction log.
- Devised machine learning models for transaction classification using SVM and Gradient Boosted Tree classifier(XGBoost) attaining .84 and .95 F1 score to perform automated analytic based research on the new and old transaction.
- 50 % reduction in the overall testing effort was achieved using the research Framework.
- Automated validation of request & response messages for Acquirer, Issuer and Switch at IPC(Inter-process communication) level.
- The framework also supports different modes of testing Unit, Progression, Regression and online testing with other Network gateways (like Visa MasterCard etc.).
- Inline field checkpoints within IPC message.
- Was awarded Accenture Idea of the year-2014-15 for the project.