Transforming the STR process of a leading US bank

A leading sell-side global bank that offers structured products to retail, institutional and high-net-worth clients saw significant spike in trade inflows. That, combined with rising product complexity, and regulatory and audit scrutiny, persuaded the bank to seek our expertise to:

• Transform and scale up the existing structured trade review process to manage higher volumes
• Identify opportunities to automate repetitive processes
• Engineer an optimised trade review template to drive automation, flag risks and streamline resolution workflows

We drove this transformation through two core initiatives:

• Smart trade workflow automation and oversight
  • Automated new trade scoping, review prioritisation and trade allocation using VBA and Python macros with integrated daily reporting on trade inflow, ageing queries and pending documentation for enhanced operational visibility 
    
  Delivery timeline: We completed this process over nine months, during which the automation evolved from a basic trade-scoping template to a fully integrated workflow system covering allocation, reporting, control and governance.
  
  
• Trade review automation
  • API-based trade data ingestion from the risk management system: Extracted trade data using Python scripts and trade identifiers from large databases
  • Trade document scraping: Used Python scripts to identify patterns in documents (term sheets, confirmations, pricing supplements) and extract data for templates
  • Reconciliation of trade booking vs documentation: Automated matching of booking data with document-scraped information using VBA and Python templates with set rules
  • Payoff and analysis: Stimulated hypothetical payoffs to validate booking vs. documentation payout accuracy using risk model definitions
  • Query management for discrepancies: Automated the workflow to collect discrepancies and generate queries for stakeholders
  Delivery timeline: Deployed the automated review template with high-volume, low-complexity models at first and through phased integrations over a year, scaled it up to review 100% of the trades.
  

• Workflow automation and efficiency gains: The automated pre- and post-review workflows eliminated repetitive tasks, creating bandwidth for the team to focus on managing higher trade volumes and value-driven activities
• Improved review accuracy: The enhanced trade review template with built-in checks reduced manual intervention and improved review quality
• Scalable architecture: The solution supported multiple booking models and trade structuring nuances, enabling seamless expansion across asset classes and product types
• Full auditability: End-to-end audit trails were integrated within the review workflow for transparency and traceability of all trade actions
• High-impact risk mitigation: Early identification of booking discrepancies enabled timely amendments, preventing errors with high profit and loss impact year after year
• Significant efficiency gains: Review time decreased 50%, resulting in estimated savings of ~5 full-time equivalent (FTE) days per day