Counterparty credit RWA optimisation – driving capital efficiency

A European investment bank, engaged us to address populations of derivatives transactions that were failing to be processed via the internal regulatory approved expected positive exposure (EPE) calculation methodology

Our client’s derivatives portfolio (1.5m+ derivatives positions) was booked in a number of disparate front office trading systems.

Prior to arriving at the EPE calculation engine, the derivatives position information passed through a number of transformation layers from front office booking portal, through risk and finance systems.

These transformation processes often altered / removed key data attributes required for the correct valuation via the EPE engine methodology.

These transformational processes were black-box in nature.

We executed detailed data analytics in order to understand existing flows from multiple cross-business systems into the EPE calculation engine and to highlight error patterns.

A series of extractions were established from each of the data transfer points – for example from the junction between the trade capture system and the valuation engine.

The required data “recipe card” for each transaction type was then overlaid using data analytics tooling to spot patterns of discrepancies forming at each point of the data flow.

Repeatable logic ensured a monitoring infrastructure to give early warning of increasing exclusions.

The exposure of the transformation logic using repeatable data analytics technologies enhanced control of the end-to-end transaction flow.

Route cause identification allowed senior management to track and prioritise robust fixes to existing process flows to further enhance RWA optimisation.

Enhanced control mechanism reduced month-on-month volatility of RWA numbers and allowed capital forecasting to be driven with greater precision.

Programme delivered a multi-billion reduction in counterparty credit RWA.