Each institution must collect data for the estimation of all risks arising from instruments and portfolios where it has material exposures. The data collection must be sufficiently granular to support adequate modelling. This means that data collection must be (i) sufficiently specific to be attributed to risk types and instrument types, and (ii) sufficiently frequent to allow the construction of historical time series.
5.3.2
The data collection process must cover, amongst others, credit risk, market risk (in both the trading and banking books), concentration risk, liquidity risk, operational risk, fraud risk and financial data for capital modelling. A justifiable and appropriate collection frequency must be defined for each risk type.
5.3.3
The data must be organised such that the drivers and dimensions of these risks can be fully analysed. Typical dimensions include obligor size, industries, geographies, ratings, product types, tenor and currency of exposure. For credit risk in particular, the data set must include default events and recovery events by obligor segments on a monthly basis.
5.3.4
The data collection must be documented. The data collection procedure must include clear roles and responsibilities with a maker-checker review process, when appropriate.
5.3.5
Institutions must seek to maximise automated collections and reduce manual interventions. Manual interventions must be avoided as much as possible and rigorously documented to avoid operational errors.
5.3.6
The data collection process must ensure the accuracy of metadata such as units, currencies, and date/time-stamping.