Risk Data Infrastructure: Remains Floating in the Flood Area

What you need to know to build a business risk and finance reporting inventory

By Ravi Chari, Sonal Singh and David Rogers, SAS Risk Quantitative Solutions

Banks and financial institutions continue to be presented with revised or new risk-related rules. And reporting requirements are becoming stricter across the globe. Eg. Will the recent requirements of the European Banking Authority (EBA) for COREP / FINREP and the European Central Bank’s (ECB) recent regulatory requirements for AnaCredit (analytical credit data set) require grain storage with minimal delay and appropriate definitions for each data element.

Most banks started to create regulatory risk data infrastructure as part of their Basel II implementation. However, in most cases this risk data infrastructure was tactical and limited to meet the immediate Basel II requirements. It is now often seen as inadequate to meet the needs of the new stream of rules.

Today, banks are still looking to improve their risk data infrastructure to reduce compliance costs with existing regulation. The introduction of the new regulations increases an already stressed situation by requiring more detailed data (such as assets, liabilities and off-balance sheet liquidity risk items) and expected data for stress testing.

Data quality: Achilles’ risk management heel

One of the most important criteria for successful compliance with each of the above rules is the creation of an appropriate risk data infrastructure. The Basel Committee on Banking Supervision (BCBS) published principles for risk data aggregation in January 2013. The principles are structured around four broad areas: management, data collection, risk reporting and supervisory requirements (Figure 1).

The BCBS 239 principles have heightened discussions about managing risk data management from IT to senior management; However, they do not provide operational guidelines for a bank to embark on a risk data infrastructure project. In addition, there are no accepted best practices or regulatory directives for appropriate risk data infrastructure to help a bank meet various regulatory requirements from a data perspective.

What are the challenges of a risk data infrastructure and how can they be addressed? In short, some of the specific challenges that banks face are:

  • Several operating systems: Various industries have built their own systems and created scattered and inconsistent data.
  • Incomplete data: Banks often lack historical or detailed data. And it is difficult to access and combine data stored in different systems.
  • Data discrepancies: Lack of standardized data entry procedures, invalid data (for example, incorrect addresses), duplicate records, and human error result in poor data quality which is time consuming to correct.
  • Data clarity and usability: Business entities can use different terminologies for the same data entry or business process.

Banks can tackle these challenges with scattered, incomplete and inconsistent data by first building a business risk and funding reporting inventory. SAS® Detailed data storage (and the associated SAS data quality infrastructure) can be very important for starting such a business risk and funding data warehouse reporting. Building a warehouse and data quality processes solves several problems:

  • Scattered data in several operating systems: An enterprise risk data warehouse serves as an authoritative source for risk and financial reporting.
  • Data Integrity: Since an enterprise data warehouse can be the sole source of data for all analytical applications and reporting, it is much easier to reconcile the results and reports to the repository and then to source systems such as those for accounting and trading capture. The features of reconciling and teasing back data will also help banks with scrutiny.
  • Incomplete data: The company’s data warehouse enables support for a variety of risk factors, regulatory and business issues, including Basel III, credit risk, market risk and liquidity risk.
  • Data inconsistency: Adding data management processes on top of the risk warehouse ensures that there are owners and managers of all risk and financial data. Banks can track all data additions, changes or versions. And a well-developed enterprise data warehouse will have data history and offer data versioning.
  • Unreliable reporting and terminology: The company’s data warehouse must be built with a comprehensive dictionary that describes bank data elements for loans, deposits and off-balance sheet items, and provides a complete mapping of physical data structures to business conditions.

From a risk perspective, high-quality data is important for implementing effective risk strategies and ensuring compliance. To learn more about the issues that data presents when dealing with regulatory risk management, read the White Paper, Data quality: Achilles’ risk management heel.

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