CSRBB IT system and data quality

The IT systems and applications used by the institution to carry out, process and record operations, to identify, measure and aggregate CSRBB exposures, and to generate reports should be capable of supporting the management of CSRBB in a timely and accurate manner. In particular, the systems should:

• (a) Capture credit spread data on all the institution’s CSRBB exposures. This should support the institution’s measurement s ystem to identify, measure and aggregate the major sources of CSRBB exposures;
• (b) Be capable of fully and clearly recording all transactions made by the institution, taking into account their CSRBB characteristics;
• (c) Be tailored to the complexity and number of transactions creating CSRBB;
• (d) Offer sufficient flexibility to accommodate a reasonable range of CSRBB shock and stress scenarios and any additional scenarios;
• (e) Enable the institutions to fully measure, assess and monitor the contribution of individual transactions to their overall exposure; and
• (f) Be able to compute the CSRBB measures.

The IT system should be capable of recording the credit spread characteristics of the products.

The systems used to measure CSRBB should be capable of capturing the CSRBB characteristics of all products.

Risk measures should be based on reliable market and internal data. Institutions should scrutinise the quality of external sources of information used to establish the historical databases of credit spreads, as well as the frequency at which databases are updated.

To ensure the high quality of data, institutions should implement appropriate processes that ensure that the data entered into the IT system is correct. Data inputs should be automated as much as possible to reduce administrative errors, and data mapping should be periodically reviewed and tested against an approved model version. In addition, there should be sufficient documentation of the major data sources used in the institution’s risk measurement process. Institutions should also establish appropriate mechanisms to verify the correctness of the aggregation process and the reliability of model results. These mechanisms should confirm the accuracy and reliability of data.