ZIGRAM is pleased to announce the release of Transact Comply version 1.10, which introduces the capability to generate reports compatible to the FINGate 2.0 reporting framework and significant enhancements across the platform.
This feature enables registered entities (REs) to generate reports compliant with the FINGate 2.0 regulatory framework for submission to the FIU portal. The feature also introduces an automated data prefill mechanism that populates customer and transaction details directly into the report form, reducing manual entry and significantly improving reporting speed and accuracy.
We have introduced two new functions within the Rules Engine to support advanced Capital Markets compliance use cases. These functions enable the creation of rules that detect Pump & Dump and Front Running scenarios—two high-risk market manipulation patterns. With these additions, users can now configure more accurate and targeted surveillance rules, improve the detection of suspicious trading activity and strengthening overall market integrity monitoring.
We have made significant enhancements to our transaction processing engine, enabling the system to efficiently handle up to 10 million transactions within a 24-hour window. This improvement delivers higher throughput, increased scalability, and improved system performance—ensuring faster processing times, better stability under heavy loads, and an overall more seamless experience for high-volume users.
Introduced the capability to store and manage additional file types required for Capital Markets monitoring. The system now supports ingestion of IP Address & Device ID Files and Price Band Files, enabling users to build rules that rely on these enriched datasets.
Introduced a new set of Fraud Monitoring Rules designed in accordance with AFASA compliance guidelines to strengthen detection and prevention of suspicious activities within insurance transactions. These rules enhance the system’s ability to identify unusual or potentially fraudulent patterns in claim and premium behaviors.
1. Frequent Loan Order Cancellations – Generate an alert when a customer cancels loan purchase requests within [X] hours/days of the original request and the total count exceeds [Y] within a [Z] time window.
2. Repeated Early Loan Foreclosures – Generate an alert when a customer repeatedly pays off and closes loans early, where the number of such foreclosures exceeds [X] within a [Y] time period.
We’ve resolved several known issues to enhance overall application performance, delivering a more stable, responsive, and reliable user experience.
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