If we take the example of FR2052a reporting, $700 bn plus banks have already commenced daily reporting on inflows and outflows. For smaller banks, the reporting is expected to be monthly and will kick in from 2017.
Essentially, if we analyse the FR2052a data sets, bank need to report on inflows and outflows from different kinds of assets and liabilities. A comprehensive master data framework is enforced on the data sets that need to be submitted with the intention of making it easy for the regulator to compare and process data received from multiple banks.
From a change management standpoint, banks need to have in place a robust and GUI based master data management framework to maintain and manage inclusion of new codes (regulator initiated), modifications in mappings to existing codes(bank /regulator initiated) and then go ahead and generate the final datasets. Similarly, LCR reporting enforces the “HQLA” categorisation framework on the banks assets which might be subject to revisions. Thus, a critical part of a smart change management strategy for Liquidity reporting is a robust master data management framework and data classification engine.
Another important piece is capturing data and having a data model that can support an exhaustive list of attributes to ensure all aspects around counterparty, nature of transaction, currency and product are covered. From a stress testing standpoint as well, having a model that can enable setting up conditions on a variety of parameters is a necessity as this is another area which is likely to evolve.
When it comes to Liquidity reporting a strong data foundation overlaid by a robust master data management framework can equip banks to better handle internally triggered or externally initiated changes that come their way.