The data challenge
Most banks that have adopted a strategic approach have hit one of the main challenges almost at the outset. What data is needed, where is it, what form is it in, how do we make it consistent? An under-estimation of everything to do with data is a common theme.
The data in nearly all banks is far too dispersed and inconsistent. The new compliance and accounting architecture has to be implemented on top of infrastructures that have grown up over a long period of time, with too little planning, resulting in today’s legacy and siloed environments. In all but the lowest tier banks, data resides in myriad transactional systems, other applications, databases and spreadsheets; other relevant data might reside in external sources. Mergers and acquisitions bring additional infrastructure
and organisational headaches. The transaction systems have their own data models and processes. This does not only apply to static data. Trading systems have their own risk calculations. A common piece of data such as ‘average monthly balance of account’ will be defined and calculated differently and assigned a different meaning across disparate operational systems. Take the example of ABSA Bank, which was a coming together of four banks in South Africa. Prior to its introduction of a bank-wide Business Intelligence (BI) strategy, it had a mix of systems across its 50+ business units. The units had bought their own systems. There was a lot of duplicate work that could be classed BI, with more than 30 related projects involving 200 internal staff plus external resources. There were 1200+ reports, typically semi-automated and often developed over the years to meet the requirements of individuals within the bank. There might be five answers to the same question, said ABSA general manager, Cornie Victor. ‘There was no single version of the truth.’
The need for considerable historical data further complicates matters. This might not have been captured in the past. If it does exist it might be in paper format and again in multiple sources. One banker, when speaking about his institution’s Basel II project, explained that when it had recently bought a small regional bank, it had found that this institution had stored all of its historical data off-site, in paper format; a fire shortly before the takeover meant it had literally all gone up in smoke. Even in a situation where the historical data can be located
and brought into the compliance architecture, there will be a problem of scale for institutions of any size – three to five
ABSA Bank HQ
years’ worth of historical data on a sizeable mortgage book, for instance, will require a pretty large data repository. Indeed, the historical data aspect is likely to be toughest for
retail banking not only because of scale but also because of the past lack of collection of such data. Risk models in wholesale banking have been prevalent since the mid-1990s but this is not the case for retail banking. The way to resolve the data issues are considered later in this
report but are likely to include some or all of the following: a large reconciliation task; a move to a bank-wide data model, embracing common definitions and meta data; ETL tools to transform disparate data into the common formats; new roles within the bank to manage the new environment including some form of ‘data guru’; and corporal or capital punishment (well, not quite) for anyone who is subsequently found guilty of proliferating data within the organisation but outside of the corporate architecture. There must be no exceptions and no fiefdoms.
Risk Management Systems & Suppliers Report |
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