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Formerly known as Global Research & Risk Solutions

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May 29, 2026 Content Type Case study

Improved deal coverage efficiency, scalability for a global investment bank via automation

May 29, 2026 Content Type Case study
 
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    Background

     

    • The client required a standardized and scalable framework to bucket loan portfolio assets into high-, medium- and low-risk categories 
    • The client’s legacy process relied on static models and deal-specific customizations, with inconsistent methodologies and high risk of errors on account of manual inputting of data posing multiple bottlenecks
    • Each client engagement also required separate versions and periodic manual updates, significantly extending turnaround times and increasing operational overheads.

    Our solution

     

    • Process redesign: We redesigned the risk classification methodology into a centralized, standardized framework, with clearly defined risk parameters, objective classification criteria and structured governance controls to reduce subjectivity, enhance transparency and ensure consistent application across client mandates.
    • Dynamic model architecture: We also transformed static, deal-specific components into automated, parameter-driven modules, enabling scalable deployment across clients and sectors.
    • Automated update cycle: We implemented straight-through data integration and model refresh capabilities as well, eliminating repetitive manual rebuilds and ensuring consistency across recurring publications and client reports.

    Client impact

     

    • Reduced turnaround time: The redesigned process improved operational efficiency by >70%, thereby enabling faster delivery of research updates and thematic reports to clients.
    • Enhanced scalability: The process also provided for simultaneous coverage of multiple portfolios and processing of higher data volume, as well as 5-6x increase in data volume processing without a proportional increase in analyst effort.
    • Greater focus on strategic value: The framework also freed up analyst time through >80% reduction in manual intervention, providing more time for deeper analysis and differentiated research.
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