Data Analytics: Elimination of discrepancies and material reduction in regulatory risk
Elimination of discrepancies and material reduction in regulatory risk
A real-life case study
Our client, a household-name bank, implemented a new liquidity reporting platform, but found on first run that there were discrepancies of £100 million versus the legacy system.
It was unclear which data was wrong: the old, the new or both. Whatever the case, there was a significant risk of material regulatory censure.
Our challenge was to identify the source of every difference and error from a complex, multi-sourced and disparate data set to ensure 100% (zero exception) alignment and accuracy of company-wide regulatory reporting.