Introduction

In the face of unrelenting regulatory pressure, and to continue to improve risk management capabilities, the need to establish and maintain effective risk data aggregation processes and capabilities continues to be a key priority for the world’s leading investment banks. However, banks cannot easily improve their decision making and risk management without high quality risk and financial data.

In January 2015, the Basel Committee on Banking Supervision published its second progress report on BCBS239, “Principles for effective data aggregation and risk reporting”. The report revealed that a number of Global Systemically Important Banks (G-SIBs) are struggling to comply with the 11 banking principles enshrined in BCBS239.

The challenge facing banks is that inside their institutions there are a plethora of disparate data sources, systems and unintegrated processes that support different business lines and products. Each source of risk or finance data will frequently have its own format, standards and interfaces. Moreover, each firm will likely have its own terminology, language and local dialects for describing risk and finance information. This lack of standardisation surrounding data makes it difficult to extract meaning and value, and compounds the difficulty of performing analyses of large aggregated data sets. Risk data needs to be calculated, encoded, structured and presented in a commonly understood language - what we need is a ‘lingua franca’ for risk and financial data.

The need for a common language

Throughout history humans have had to deal with a similar challenge, communicating with one another, particularly where communities and societies use very different native languages.

In Medieval Europe there were countless languages and dialects, so Lingua Franca evolved and became a de facto common language, essential for business and commerce, used between peoples of diverse and very different states and kingdoms. In finance we have a similar problem with different firms and groups inside those firms having their own languages, conventions and specifications for risk and financial information. Even where industry standard terminology exists, there remains the issue of interpretation and implementation.

Increased focus on data aggregation capabilities has also promoted interest in the need for taxonomies. In the context of developing a lingua franca, the definition and labelling of data within hierarchies is key, but it is by no means the only activity, nor is it an end in itself. Some firms have become bogged down in multi-year programmes to define complex product hierarchies with associated attribute mappings, to determine how risk data is aggregated and stored, rather than paying attention to essential aspects such as risk measure conventions. 

Establishing a suitable lingua franca

The fundamental challenge in the aggregation of risk and financial data is the need to aggregate data that shares common qualities, essentially aggregating ‘apples with apples’ not ‘apples and bananas’.

To overcome these challenges, a suitable lingua franca should establish the following:

1. Clear and consistent labelling of risk and financial measures
2. Consistent conventions for the calculation of measures
3. Time and version tagging of measures to ensure that the correct data sets are retrieved and aggregated
4. Consistent and complete coordinate information and static data; reference data such as trading books, portfolios, accounts, and legal entities must be standardised and ascribed to risk and finance measures
5. Metadata to support data quality monitoring and analysis

Conclusion

If banks are to improve their risk data aggregation capabilities without resorting to building massive data refineries, they require a lingua franca for data, underpinned by strong data governance. An enterprise-wide lingua franca should lay down risk and finance standards for labelling, calculation, timing, trade and reference data and aggregation metadata. Taken together, with appropriate ownership and governance, these will ultimately enable and sustain large-scale, meaningful aggregation and reporting.