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Finance analytics CoE- technology hardware

Finance analytics CoE

Finance analytics CoE

Analytics CoE potential in Finance

Business leaders and decision makers often face challenges in getting their hands on suitable analytics. Through our work we have identified a number of issues within Banks and financial services; a lack of controls, poor data quality, duplication of effort and disparate sources means meaningful information is difficult to obtain and understand. How do we overcome these obstacles? The answer lies within a Finance Analytics CoE.


Why Finance?

If we were to place a CoE within another function, Marketing for example, they would not have the same understanding of controls leading to questions around data quality. They would not have the same relationships with the business to truly understand their requirements or have the same ownership over financial data. 

Finance is uniquely positioned to house the Analytics CoE for a number of reasons. First, as a highly governed and controlled function, Finance is seen as a trusted source of information for external and internal reporting. Second, a CoE within Finance would extend the natural business partner relationship to leverage the exclusive level of access to enterprise information and provide reliable MI across the organisation. Third, it would facilitate financial data ownership since commercial decisions stem from the synthesis of financial data.



Key benefits of a Finance Analytics CoE to the Finance function and the bank include:

  • A consolidated approach to slim down and streamline processes reducing costs
  • An honest view of the numbers from a central source to reduce bureaucracy
  • More meaningful analysis with clear accountability to add value to the business

Recommended Operating Model

Role in the CoE

There are three key roles that will emerge at the core of the Finance Analytics CoE. These individuals, together with the organisation’s leadership, will act as a force for driving change, aligning outputs with business strategy and managing links between stakeholders. 

  • Business partner / strategist: leverage strong understanding of external markets to evaluate threats and opportunities against the strategic direction for the business and influence the business model
  • Financial data modeller / scientist: Apply STEM or D&A background to execute complex financial models and advise the business on the financial and business impacts of different scenarios
  • Business planning analyst: Utilise extensive business knowledge and deep technical FP&A expertise to handle interactions between different business groups and communicate information effectively

Behavioural skills

Behavioural skills are equally, if not more, important as data and technical skills. These skills encompass data and technology utilisation, individual behavioural competencies to interact with business stakeholders to understand their requirements and technical Finance knowledge necessary to carry out value adding tasks. Organisations need to manage talent pools to equip responsible parties with adequate skills within the Finance Analytics CoE.

Governance and process

A centralised governance structure is recommended to remove inefficiencies in the process. Key benefits to centralisation include a single source of information with a single point of escalation, optimisation of economies of scale and reduction in the duplication of effort. 


Data should incorporate the following elements:

  • Business drivers: MI to be business centric and provide meaningful data to users
  • Risk drivers: Data should utilised to mitigate risk and improve controls
  • Definitions: A single definition to drive consistency of reporting
  • Master data: A single source of data to eliminate duplication and improve data quality

Systems and tools

Cloud is the clear choice for systems. The reasons behind this are twofold: (1) Cloud tools have the ability to set up the capability a per organisational requirements to manage large volumes of data from disparate source systems and (2) the cloud has the ability to process complex calculations on vast amounts of data in the blink of an eye. A single source of data in a centralised hub will drive accuracy and reliability of information. The systems and tools in place for the CoE should be agile and foster collaboration within the organisation to “naturally generate” commentary and reduce “armies” of commentary writers. 

There are five distinct levels of maturity in the Finance analytics CoE:

  • Infancy: Prove out on one selected FP&A process (planning, MI or cost allocation) to build the operating model
  • Technical adoption: Build the technology to deliver the operating model for the selected process, integrating systems and data
  • Business adoption: Roll the operating model out to the business and embed it within the business to ensure that it sticks and is operating effectively
  • Enterprise adoption: Take the operating model and systems and apply to the remaining FP&A processes
  • Data and analytics as a service: A fully functioning CoE able to service both the business needs and the group requirements 

Click below for more perspectives on the transformative role of the Finance functions in banks.