2-May-03
by Mario Orphanou and Siegfried Van Puyvelde
With increasing internal and regulatory pressure to control the full transaction processing chain, Banks would be wise to look into addressing issues on reference data management. As there are many possibilities to significantly reduce costs (such as administration and data vendor costs) in addition to reducing operational and reputation risk, this is an opportunity that shouldn’t be neglected in the current economic situation.
From an operational point of view, banking has become an industry that is largely dependent on the accuracy and the speed by which data is processed. Banks’ universal move towards Straight Through Processing or STP marks a general industry trend to achieve these goals. More and more of a bank’s processes are integrated in a continuous chain of processing, yet the one element that is not taken care of in a sufficient way is the raw material: the data on financial instruments.
As data is the lifeblood of any financial institution, an inadequate data strategy results in inefficiency in the purchase, administration and deployment of data. Data integrity is vital; using suspect data can lead to far-reaching problems for risk management, performance measurement, trading and portfolio accounting.
A survey by PriceWaterhouseCoopers in November 2002 concluded that ‘the forthcoming implementation of the Basel Accord will force banks to tighten lax procedures. Over the next two to four years banks will be forced to devote increased resources to redesigning and rebuilding institutions' systems and data management infrastructure.’
How do financial institutions manage instrument data today? Typically, the process looks like this: Data is fed from data providers (e.g. Telekurs, Reuters, Bloomberg, WM etc.) into specific systems. Various data providers may be used for different systems. The instrument data available in one system may not be equivalent to that in another system. Errors are regularly found after their usages, and much time and resources are spent in their administration.
These arguments are supported by a survey in the magazine "STP Straight- Through-Processing" in February 2001, which reported that:
- Over 75% of the fund managers and brokers surveyed invest at least two man years per annum repairing price data supplied by external vendors.
- 90% of repairs are made after the data has been utilised in a critical process.
- 30% of client details held contain errors that are undetected.
- Only 5% of firms employ tools that intelligently detect and repair errors automatically.
We believe that, for solving these problems, banks should centralise collection and administration of common and duplicated data such as instrument data (including prices, static and reference data, corporate actions, etc.) as well as other common infrastructure data such as Currencies, Countries, Third Parties, etc.
A Central Securities Master File solution does just that. The benefit of having a central repository is that the data only need to be administrated in one place. This amounts to reduced administration costs, improved client service through more efficient processes and reduced reconciliation issues due to the fact that all the bank’s systems are using the same data source.
To help banks achieve these objectives, Odyssey developed Nexus, a modular and evolutionary solution for the centralised management of reference data. Such a tool combines Data Collection, Data Filtering and Administration, as well as Data Storage, Data Request and Publication.
One of Odyssey’s clients took the test and measured the effect before and after implementing Nexus. As a result, this Bank has not only saved on administration and data vendor costs, but it also has reduced its operational and reputation risks.
1) Administration costs saved- Improved workflow process
Through an automated exception identification process users do not have to identify and correct the results of undesired data changes. Key data issues are identified by the system through automated and user definable filtering. The Bank also reported that data service selection has been optimised through Plug and Play access to data vendor services and cost savings in support through the low maintenance overhead.
2) Data Vendor Costs Reduced
This Bank managed to save costs through more effective use of data sources and services and increased value from their remaining data spend. In addition they were able to leverage data sources that were being used elsewhere in the organisation at little or no extra cost. This flexibility has allowed the Bank to achieve a total of 50% cost saving with data vendors.
3) Operational risk reduced
As such a system identifies the potential problems before they appear in the business, this figure is difficult to quantify. However they reported that Nexus has allowed them to keep an impressive Zero loss (from pricing and income errors) over the last couple of years, whilst respecting all deadlines and meeting all regulations ahead of market requirements.
4) Reputation risk reduced
Again there is a good track record, which Nexus has helped to maintain. Because the system has freed an average of 35% of personnel time, external or internal Clients that do have issues have seen a vast improvement in the service level provided, as staff can direct their focus on any errors rather than on the collection of data.
In an effort to implement Straight Through Processing, many banks overlook the importance of raw data—notably instrument data—to every financial institution.
As regulatory, client and internal pressure mount, data quality becomes of increasing importance. Moreover, fast and efficient processing is a first step towards meeting the T+1 requirements. By centralising the collection, filtering and storage of all instrument data (prices, static data, corporate actions etc.) Banks can reduce risk and obtain important cost savings in one effort, resulting in a quick ROI.