JS Bank advances digital infrastructure by partnering with Astrik, Grant Thornton

New system will strengthen risk management, data-driven decision-making capabilities: CEO JS Bank

JS Bank declares cash dividend of Rs1.2 per share. PHOTO: EXPRESS

Astrik, in partnership with Grant Thornton, has officially signed an engagement letter with JS Bank to develop and implement a comprehensive IFRS 9–compliant Expected Credit Loss (ECL) and Effective Interest Rate (EIR) automation system.

The signing ceremony took place at JS Bank’s head office in Karachi, marking a major milestone in Astrik’s ongoing efforts to drive digital transformation across Pakistan’s banking sector.

The collaboration combines Astrik’s technological expertise with Grant Thornton’s financial and regulatory experience to deliver a robust, enterprise-grade solution that will automate JS Bank’s IFRS 9 ECL and EIR computation processes. The system will integrate multiple reporting modules within a unified, auditable framework—replacing manual spreadsheets with a fully automated, secure, and regulatory-compliant platform.

Speaking at the ceremony, JS Bank’s President and CEO said, “This system will enhance the accuracy of our IFRS 9 reporting while strengthening our overall risk management and data-driven decision-making capabilities.”

Khurram Jameel, Partner at Grant Thornton, noted, “Our partnership with Astrik and JS Bank demonstrates how strategic collaboration between financial and technology experts can set new standards for compliance automation in the finance industry.”

Astrik’s Founder and CEO added, “Astrik’s mission has always been to bridge technology and compliance through intelligent automation. This project is a testament to how we’re helping financial institutions achieve not just regulatory efficiency but also analytical depth and governance transparency.”

The newly developed ECL and EIR automation system will form the cornerstone of JS Bank’s credit risk infrastructure, featuring real-time computation, configurable scenario testing, and a complete audit trail of model assumptions, data, and results.

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