Counterparty Credit Risk

  • xVA’s are one of the most challenging areas of finance in terms of computations but very much also in terms of data: massive sizes and numerous sources often require a heavy involvement from IT departments, potentially leading to longer delays and additional efforts from xVA desks and risk officers to process their data.
  • Limited data capabilities are a limiting factor for CCR and xVA management. For instance, keeping nearly 2 trillions of Mark-to-Future intermediary points of all Monte Carlo paths: 


  • Unfortunately those points are often discarded because that much data cannot be handled efficiently in existing set-up’s

  • ICA’s RAFAL results management database can absorb a massive Mark-to-Future cube at 350 Mo/s/node and run queries at 700Mo/s/node  
  • Data can be handled by users with the flexibility of a pivot table with the capacity to access the most granular level (eg. deal leg)
  • RAFAL provides the ability to add proprietary analyses on the whole data set with customizable Python calculators allowing users to autonomously compute metrics as complex as CVA under CSA's 


  • Counterparty Credit risk is better monitored and handled
  • The Credit Risk team can resolve their audit point on BSBC 239 (in accessing granular intermediary data, “what-if” scenarios) and can implement specific risk analysis
  • Risk officers enter a new dimension of opportunities to analyse credit risk with more speed and comprehensiveness, assess the relevance of limits, test macro hedges on tail scenarios, run back testing, clustering, what-if simulations...
  • Capital charge is much better challenged and anticipated (eg. through pre- and post-trade marginal impacts…) 


  • would mean significant faster “what-if” simulations, improving data analysis and help challenge capital charges.  
  • On top of being BCBS 239 compliant an easy access to those intermediary results would allow to solve several needs: cVaR attribution and explanation, backtesting... 


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