another level of complexity. While IBM’s model in particular has a reasonable following, mainly among large banks, many other banks decide that the in-house definition route is preferable. For instance, Toronto Dominion considered IBM’s ‘Dublin model’ as it called IFW (IBM Dublin has been the long-standing home of the data model), as well as Oracle’s Peoplesoft-derived financial services data model but decided that off-the-shelf models were too expensive and would need extensive extending and reshaping, so were deemed not worthwhile. However, a number of banks have invested in third-party models down the years, to try to bring consistency for other reasons, such as system integration or development. ABSA Bank felt ‘ahead of the game’ when it came to defining a single vocabulary and data model for the compliance arena, as it had adopted IBM’s content models, BDW, some time ago. This model had been tailored over time and formed the basis for what effectively would be the ‘ABSA Information Model’. The three attributes needed, as set out by the bank’s Dr Conor Hughes, were ‘consistency, uniformity, and reliability’ of data. Bank of New York took the Peoplesoft data model – the
bank was already a customer for human resources and funds transfer pricing, and opted for the Peoplesoft EPM warehouse for its central repository. The Peoplesoft data model effectively evolved into a home-grown model. In fact, by the end, the estimate from the bank was that probably less than ten per cent of the original would be left. Also within the Oracle camp (as Peoplesoft is today) there
is now the Industry Reference Model for banking. This is a process map, with meta data and business rules. It has its roots in the data model of Oracle’s I-flex-derived Flexcube and within consulting projects. A first offering built on top of the model was unveiled in April 2009, an enterprise-wide limit and collateral management solution which incorporated a number of other Oracle components as well, including those for document management, identity management and Business Intelligence, plus its Fusion Middleware. Some banks will have a data model in place, some will have parts of one – either bought or built – and some will more or less be starting from scratch. For a small bank, centred around one core banking system, this should be easier and that core system could well form the basis for the data model. There are a number of specialist data companies, as well as
utilities (particularly Swift when it comes to payments-related data). Two reference data stalwarts are Accuity and Eiger. Accuity has The Global Banking Resource (TGBR) and IBAN Payment Resource. It has had an IBAN directory since late 2001 and was the first to market with an IBAN to BIC database. Eiger, bought and subsequently rebranded by Experian, is
another BIC and IBAN specialist, which competes head-on with Accuity.
CB.Net was a player in the same space and was fairly influential, particularly after it gained mandates in 2007 from the European Payments Council (EPC) to provide its SEPA adherence repository (Swift was among others to tender) and then from the EBA for the reference data part of its Priority Payments scheme. Reflecting a wave of consolidation in late 2008 and early 2009, it was acquired by its rival, Accuity. Another supplier was Siperian, which was acquired by Informatica in early 2010. It was to have provided the data cornerstone for the project at Icelandic bank, Glitnir, dubbed One-G. The bank – prior to the financial crisis – was seeking a unified view of customers, products and relationships across all lines of business. The bank’s managing director, shared services office, Pall Kolka Isberg, cited a number of reasons for choosing Siperian’s MDM Hub. ‘The structure is very good and in terms of ease of implementation it is probably the most simple.’ The vendor, its commitment to One-G and price were also influences, he said, and its offering was felt to be more of a standard product than those of the competition. Glitnir had started to implement it for employee data; master data was to have been added, area by area. As new systems were implemented within the One-G project, so they were to have been linked to Siperian; for legacy systems, this was to be done where it was felt to make sense. The Siperian system had been intended to support the bank’s sales, marketing, risk management and product development. Thomson Reuters has also moved into the reference data space. Its DataScope service was launched at the start of 2006 for pricing and reference data information, including corporate actions. In mid-2007 it added counterparty data. The data can be used to populate a bank’s systems and there are mapping facilities for converting it into the required formats. There is a compliance aspect to the service, including data from sanctions lists, regulatory enforcement lists and law enforcement lists. ‘The big gap was the entity,’ said Thomson Reuters’ head of
regulatory compliance data, enterprise information, Jonathan Hodgson, at the time of the extension to counterparty data. In the past, it had been extremely difficult to secure budgets for entity information projects ‘because it sort of works’ but he felt this was changing. While legacy systems had mostly been interfaced, the reference data was still a problem and this was being worsened by new regulations such as MiFID. The Thomson Reuters service is based on prime sources and is maintained by the company’s analysts, with corporate actions layered on top. Users can drill down to the original sources, such as a registrar’s record, annual report or SEC 8-K form.
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