Integration of an exception based daily front to back P&L Analysis and Control process across business divisions for a large investment bank

Client: Large Investment Bank

 

Objective

 

To help a large Investment Bank implement an exception-based daily front-to-back P&L Analysis and Control Process across business divisions on its strategic risk management systems. The goal was to reduce the unexplained P&L between RBPL and Market Move attribution P&L and set thresholds for exception-based monitoring.

 

Challenge

 

P&L attribution must meet rigid criteria for desk-level performance and approval to permit use of internal models for capital calculations. For that reason, a comprehensive Risk-Based P&L (RBPL) approach based on sensitivities is needed to reduce the unexplained P&L between actual and theoretical P&L.

 

CRISIL's Solution

 

To meet the Client’s control and compliance requirement, CRISIL followed a comprehensive approach ensuring end-to-end data validation and reconciliation of RBPL and Profit and Loss Attribution (PLA) data and related processes and platforms for two business divisions.

  • Data
    • Identified and sourced the required data across front-office (FO) and back-office (BO) applications:
      • Trade, Position and P&L data
      • Sensitivities
      • Market and Reference data
    • Established common identifiers for data across multiple risk systems and data sources within each business division
    • Resolved data gaps and data quality issues to ensure completeness and accuracy of data used in reconciliation
  • Process
    • Conducted end-to-end data validation and reconciliation for books, positions, and PAA breaks between FO risk systems, Product Control Data Warehouse and Strategic Risk Data Store
    • Performed Root Cause Analysis of P&L breaks observed in attribution process
    • Co-ordinated with appropriate FO, BO and application teams for explaining and resolving P&L and data gaps
  • Platform
    • Developed automated reconciliation tool to overcome the challenges of dealing with huge data volumes during testing and reconciliations of:
      • RBPL attribution to traditional market-move P&L attribution from FO risk systems;
      • New PAA data flows from FO against legacy adjusted PAA data in BO

Client Impact

 

  • Helped the bank meet the regulatory (FED, FINMA) and internal (Product Control) requirements to implement a consistent risk-based P&L methodology across the strategic risk systems for T+1 reporting
  • Integration of FO-generated risk sensitivities with P&L reporting (sent to Finance) provided for a unified business process across business clusters. This in turn improved risk aggregation accuracy and consistency across the business
  • Reduced initial unexplained P&L position by more than 50%, thereby ensuring accuracy of P&L flowing into general ledger from sub ledger

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