Published by Gbaf News
Posted on January 7, 2017
Recently, the Bank of England published its third concurrent stress test results of the UK’s banking system. Based on the Bank’s new approach, this stress test is perhaps the severest yet in examining the resilience of the banking system. Data plays a critical role in the complex process of stress testing, and most stress tests are conducted wholly or partially via spreadsheet-based models in banks and financial institutions. The issue is that the use of these spreadsheets and other end user computing (EUC) applications is uncontrolled, which potentially threatens the integrity of data used and therefore the accuracy of output from the stress test models.
Banks and financial institutions, who invest heavily in IT systems, are comparatively lax in the management of their spreadsheet and EUC landscape, even though it underpins their regulatory compliance and model governance initiatives. In particular, IT departments in banks prefer the use of enterprise business applications, so gaining their mind share for proper use of spreadsheets is often a challenge. Nevertheless, aware of the reasons of why users are drawn to EUCs, regulators are now demanding that banks demonstrate transparency around the ecosystems of tools that feed their stress test models.
Managing the spreadsheet landscape is a challenge
Henry Umney
EUCs are a double-edged sword. They are quick and easy to deploy, facilitate agility and deliver flexibility in evolving market conditions and advancing regulations. At the same, they are extremely difficult to manually manage and control.Spreadsheets contain a vast amount of data, stored in multiple sheets, making discrepancies difficult to identify. This is further compounded by linkage of these applications to each other via formulae, creating an environment where changes and discrepancies are not visible, often occurring in data not intended to be viewed after initial data input. With banks depending on 100’s of spreadsheets to support their governance models, even a single data error in one file can proliferate across the organisation’s wider EUC landscape, feed inaccurate data into a model to produce incorrect outputs.
Furthermore, spreadsheets are often shared and transferred between users, resulting in multiple versions of the same documents, but only one of which is up to date. If they are not stored and labelled correctly, subsequent users are unable to identify which spreadsheet is the current version containing up to date data, potentially causing discrepancies from the use of old or incorrect data.
Why automating the EUC management process is essential?
A central aspect of data quality is transparency, and banks need EUC management to establish how data is created and where the transformations in the figures and stress test models are occurring. In doing so, they can verify the processes and controls to ensure good quality data.
Technology can facilitate the adoption of best practice processes to ensure data quality by embedding governance into the business operation, supporting everything from creation of new EUC applications through to eventual decommissioning of these files. Enabling banks to understand and control the entire data ecosystem that surrounds the stress test model can provide a means to establish what type of EUC the data is coming from – e.g. spreadsheets or access databases; whether it isa single spreadsheet or multiple spreadsheets that feed data into the model; and what the data linkages between the various data feeds are etc. This visibility comes from a process of discovery including scanning file shares and repositories; as well as analysing the overall EUC estate structure, properties and content. Banks are then able to rank the inventory of files by the level of risk(or materiality) they pose based on the risk appetite of the organisation, providing holistic view of the complex web of data flows, on an ongoing basis.
A technology-led approach to EUC data quality management eliminates the need for manual checking, which is extremely inefficient.Even though the adoption of EUCs for modelling delivers initial benefits to users such as speed, reliability and costs – compared to the absence of a technical solution for a new operational business requirement in the enterprise system – there are challenges. The use of EUCs require manual operational processes such as augmenting and aggregating data. These spreadsheet processes can require multiple people to update and review the contents and as such users spend a lot of time double checking the EUCs’ data integrity, negating some of its original advantages.
This manual effort is hiding a huge cost to organisations. Consider this: for arguments sake, there is a population of 100 operational EUCs with an average of 8 hours per month spent on each application at a fully costed employee rate of £50 per hour. This means that each EUC costs £400 per month or £4,800 per year to operate. For the full EUC population, this translates into £480,000 for the year.
Additionally, a technology-led approach to EUC data quality management helps credibly demonstrate the validity of stress test models and the accuracy of the corresponding outputs to satisfy the regulators. EUC management solutions enable banks to set up data change management processes and control mechanisms, supported by an audit trail to ensure that the integrity of the data is always maintained.
It’s important to note that even with automation of EUC management processes, users are able to add expert judgment by altering data sets in spreadsheets to improve the alignment between theoretical calculations and the real world. The automation offered by technology solutions also facilitates re-attestation of the models, and tools that feed them, so banks can periodically re-evaluate the models to ensure that they are indeed working as desired by the organisation.
Data quality and integrity underpins the success of all model governance, not just stress tests. Given that spreadsheets and other EUCs will continue to be used for model development in the foreseeable future, automating EUC management processes makes spreadsheet usage safe and in fact favourably contributes towards the larger risk management efforts of banks.
About the author
Henry Umney is Vice President of Sales at ClusterSeven and is responsible for the commercial operations of ClusterSeven, overseeing globally all Sales and Client activity as well as Partner engagements.Henry brings over 20 years of experience in sales and account management in financial services. Prior to ClusterSeven, he held the position of sales director in Microgen, London and various sales management positions in AFA Systems and DART, both in the UK and Asia.
Recently, the Bank of England published its third concurrent stress test results of the UK’s banking system. Based on the Bank’s new approach, this stress test is perhaps the severest yet in examining the resilience of the banking system. Data plays a critical role in the complex process of stress testing, and most stress tests are conducted wholly or partially via spreadsheet-based models in banks and financial institutions. The issue is that the use of these spreadsheets and other end user computing (EUC) applications is uncontrolled, which potentially threatens the integrity of data used and therefore the accuracy of output from the stress test models.
Banks and financial institutions, who invest heavily in IT systems, are comparatively lax in the management of their spreadsheet and EUC landscape, even though it underpins their regulatory compliance and model governance initiatives. In particular, IT departments in banks prefer the use of enterprise business applications, so gaining their mind share for proper use of spreadsheets is often a challenge. Nevertheless, aware of the reasons of why users are drawn to EUCs, regulators are now demanding that banks demonstrate transparency around the ecosystems of tools that feed their stress test models.
Managing the spreadsheet landscape is a challenge
Henry Umney
EUCs are a double-edged sword. They are quick and easy to deploy, facilitate agility and deliver flexibility in evolving market conditions and advancing regulations. At the same, they are extremely difficult to manually manage and control.Spreadsheets contain a vast amount of data, stored in multiple sheets, making discrepancies difficult to identify. This is further compounded by linkage of these applications to each other via formulae, creating an environment where changes and discrepancies are not visible, often occurring in data not intended to be viewed after initial data input. With banks depending on 100’s of spreadsheets to support their governance models, even a single data error in one file can proliferate across the organisation’s wider EUC landscape, feed inaccurate data into a model to produce incorrect outputs.
Furthermore, spreadsheets are often shared and transferred between users, resulting in multiple versions of the same documents, but only one of which is up to date. If they are not stored and labelled correctly, subsequent users are unable to identify which spreadsheet is the current version containing up to date data, potentially causing discrepancies from the use of old or incorrect data.
Why automating the EUC management process is essential?
A central aspect of data quality is transparency, and banks need EUC management to establish how data is created and where the transformations in the figures and stress test models are occurring. In doing so, they can verify the processes and controls to ensure good quality data.
Technology can facilitate the adoption of best practice processes to ensure data quality by embedding governance into the business operation, supporting everything from creation of new EUC applications through to eventual decommissioning of these files. Enabling banks to understand and control the entire data ecosystem that surrounds the stress test model can provide a means to establish what type of EUC the data is coming from – e.g. spreadsheets or access databases; whether it isa single spreadsheet or multiple spreadsheets that feed data into the model; and what the data linkages between the various data feeds are etc. This visibility comes from a process of discovery including scanning file shares and repositories; as well as analysing the overall EUC estate structure, properties and content. Banks are then able to rank the inventory of files by the level of risk(or materiality) they pose based on the risk appetite of the organisation, providing holistic view of the complex web of data flows, on an ongoing basis.
A technology-led approach to EUC data quality management eliminates the need for manual checking, which is extremely inefficient.Even though the adoption of EUCs for modelling delivers initial benefits to users such as speed, reliability and costs – compared to the absence of a technical solution for a new operational business requirement in the enterprise system – there are challenges. The use of EUCs require manual operational processes such as augmenting and aggregating data. These spreadsheet processes can require multiple people to update and review the contents and as such users spend a lot of time double checking the EUCs’ data integrity, negating some of its original advantages.
This manual effort is hiding a huge cost to organisations. Consider this: for arguments sake, there is a population of 100 operational EUCs with an average of 8 hours per month spent on each application at a fully costed employee rate of £50 per hour. This means that each EUC costs £400 per month or £4,800 per year to operate. For the full EUC population, this translates into £480,000 for the year.
Additionally, a technology-led approach to EUC data quality management helps credibly demonstrate the validity of stress test models and the accuracy of the corresponding outputs to satisfy the regulators. EUC management solutions enable banks to set up data change management processes and control mechanisms, supported by an audit trail to ensure that the integrity of the data is always maintained.
It’s important to note that even with automation of EUC management processes, users are able to add expert judgment by altering data sets in spreadsheets to improve the alignment between theoretical calculations and the real world. The automation offered by technology solutions also facilitates re-attestation of the models, and tools that feed them, so banks can periodically re-evaluate the models to ensure that they are indeed working as desired by the organisation.
Data quality and integrity underpins the success of all model governance, not just stress tests. Given that spreadsheets and other EUCs will continue to be used for model development in the foreseeable future, automating EUC management processes makes spreadsheet usage safe and in fact favourably contributes towards the larger risk management efforts of banks.
About the author
Henry Umney is Vice President of Sales at ClusterSeven and is responsible for the commercial operations of ClusterSeven, overseeing globally all Sales and Client activity as well as Partner engagements.Henry brings over 20 years of experience in sales and account management in financial services. Prior to ClusterSeven, he held the position of sales director in Microgen, London and various sales management positions in AFA Systems and DART, both in the UK and Asia.
Explore more articles in the Top Stories category
