As we approach the end of 2019, we can still sense that the Basel Committee on Banking Supervision (BCBS) 239 standard continues to be an issue of great concern to banks, auditors, regulators and investors.

The standard, published in 2013, highlights the "Principles for effective risk data aggregation and risk reporting." It was created as a response to the 2007 global financial crisis aiming to support the financial stability of financial institutions through consolidating their risk data aggregation (RDA) capabilities and risk reporting practices.

According to BCBS 239 definition, RDA means “defining, gathering and processing risk data” and “this includes sorting, merging or breaking down sets of data.” By looking into the above definition, it becomes clear that an obvious path to reach what is required by the BCBS is the use of Big Data and Data Analytics.

On one hand, Big Data provides a blank canvas for reorganisation of data structures, allows ingestion of different sources of data, easy transformation of data and homogenisation of outputs. On the other hand, Data Analytics allow manipulation of data and provides the tools to create reports and datasets for risk and financial management purposes.

Furthermore, using both tools together open a world of opportunities to explore customer, contract and risk data and allow innovation and value creation in several areas such as credit and risk analysis, risk hedging, accounting decisioning as well as marketing, product creation, customer relationship management, among others.

The adoption and implementation of Big Data and Data Analytics solutions represents a deep organisational transformation. This implies major short and medium term challenges but at the same time it triggers impactful advantages in the longer run. Some of which are discriminated below:


  • Overcoming resistance to change while enhancing collaboration among different departments;
  • Implementing new data governance principles and transforming data quality processes;
  • Agreeing data ownership and finding the right data owners;
  • Building or refining data dictionaries and documentation standards;
  • Improving metadata namely lineage (data flows) and traceability (concepts calculations/derivations).


  • Improving operational risk mitigation through the reduction of manual intervention;
  • Allowing a more controlled environment by implementing simpler and more robust processes;
  • Development of more reliable data and therefore financial reports and risk management;
  • Less redundancy, repetition and time spent in low added value tasks;
  • Reduced learning costs in understanding data and concepts for everyone (business, IT, auditors, regulators, consultants, etc).

Accepting and embracing digital transformation is the main test financial institutions are facing nowadays. It must fit regulators requirements, allow value creation and improve profitability at the same time. This conjunction of factors will be key for banks to remain competitive in the times ahead of us.