Ismail Khan

Dynamic Content Manager with a proven track record at Factset Systems India Pvt. Ltd., excelling in Agile methodologies and stakeholder engagement. Spearheaded content lifecycle management, enhancing operational efficiency and team collaboration. Adept in SQL querying, I drive strategic initiatives that align with corporate goals, ensuring high-quality data products and continuous improvement.

Process improvement: successfully achieved full-time equivalent (FTE) savings through the automation of document workflows and the elimination of irrelevant documents, enhancing operational efficiency

Awards:

• Recipient of the Best Employee Award five times for consistent, high performance
• Honored with the Blue Ribbon Award four times in recognition of exceptional contributions

New hire training program restructuring

• Redesigned the new hire training program to significantly reduce trainer-led classroom time, for FY22, managed 3 batches totaling 24 new hires, cutting classroom training from 45 to 25 days by integrating hybrid and self-learning modules
• Achieved an estimated 0.32 FTE gain by optimizing training schedules and increasing the use of digital training tools
• Encouraged self-learning and inclusion, enhancing peer-to-peer knowledge exchange, and collaboration during early onboarding stages
• Improved the overall training experience and effectiveness, enabling faster ramp-up of new hires with increased exposure to varied scenarios

Quality and accuracy goals

• Met the accuracy goal of 98% by the second half of FY23, minimizing errors, and substantially improving product quality and client satisfaction

Business Development Companies (BDC) data integration

• Successfully completed Milestone 1 of the 'Private Credit Loans: BDC to Loans Migration' on September 14th, migrating approximately 20,526 BDC CUSIPs into Loans
• Achieved a major advancement in loan data integration by incorporating extensive BDC loan data into the FBL ecosystem, improving coverage and data consistency
• Executed the migration with detailed planning and rigorous testing to ensure data accuracy and minimize operational impact
• Completed full reconciliation of securities data and integrated automated task management within the application
• Validated loan identifiers across systems with post-migration checks confirming stable performance without data loss or workflow disruptions
• Realized a manual FTE saving of approximately 37 by automating the migration process
• Enhanced the loans application to display and map/remap BDC CUSIPs within the facility mapping grid, previously showing only DCS CUSIPs
• Upcoming milestones will focus on enabling automatic updates and deletions of migrated data, ensuring zero manual intervention

Data collection automation efforts

• Developed multiple proof of concept (POC) systems to extract critical financial document data across categories including agents, credit agreements, facilities, and negative covenants
• Agents POC extracts eight key fields from multi-page documents, tested on 150 documents with manual validation on a subset due to complexity
• Credit Agreement POC extracts 13 mandatory columns, tested on 100 documents with 63% accuracy against the database, and 77% manual validation accuracy
• Facilities POC extracts 27 mandatory fields, tested on 50 documents with multiple runs to improve accuracy, achieving 56% currently, and duplicate data issues have been identified and resolved as of March 2025
• Negative covenants POC extracts all negative covenants, tested on 100 documents with multiple runs, currently at 59% accuracy, manually validated on 25 documents, and recent updates have improved accuracy to 90% on new documents
• Ongoing improvements include updating AI prompts, integrating database values, and large-scale testing to enhance extraction quality
• Facilities, credit agreements, and agents extraction modules are mature and ready for the next phase of deployment and refinement
• Focus will now shift towards completing Negative Covenants extraction and finalizing remaining Credit Agreement fields
• These POCs have demonstrated feasibility and provided actionable insights on accuracy bottlenecks, guiding iterative improvements to reach business-ready solutions for automated financial document processing