HIGH PERFORMANCE IN-MEMORY COMPUTING TECHNOLOGY

The pressure to speed up the calculation of risk measures is increasing. For example, BCBS 239 (Principles for Effective Risk Data Aggregation and Risk Reporting from 2013) sets the expectations for consolidation, drill-down and forecasting capabilities. The key functionalities required for scenario analysis and stress testing are timeliness, completeness, adaptability and accuracy.

To meet the requirements, banks are to focus on their data management. Requirements cause demand for high performance computing technology for the data management and reporting needs. Interest rate risk in the banking book (IRRBB) represents a more detailed example of requirements where the increased focus on calculation and analysis speed is essential. Large volumes of data as well as heterogeneous data sets easily lead to further data management challenges. Intelligent data management and in-memory analytics provide the required speed for the calculations.

In-memory data analytics keeps all its data in the memory of the server, whereas a traditional database management structure uses disks for data storage. Compared to spinning disks, in-memory data analytics makes the calculations thousands, or even a million times faster. Instead of using the capacity in receiving and saving the data between the processor and the database, in-memory analytics can be boosted by horizontal scaling to further achieve greater calculation performance.

With intelligent data management, additional data and replacement of data during the day is also possible and can be included in the in-memory analytics. This enables intra-day and real-time risk and scenario calculations during the day, including the latest transaction updates or simulated scenario changes.

MANAGING IRRBB DATA WITH MORS

Completely automated data processing and in-memory analytics capability in MORS IRR Scenario Engine enables fast risk calculations. In addition to remarkably decreasing the daily calculation times, as well as saving cost, it also enables intra-day ad-hoc calculations for forecasting and decision making purposes.

The high-speed calculation enables repeated and automated usage of more granular data sets, whereas slow data processing is often only possible for lumped or compressed data sets. By increasing the granularity to the transaction level further enables deeper analysis and drill down to the calculated scenarios.

The speed and granularity with scenario calculations lead to more accurate forecasting, and hence more precise buffer management. The better and more accurate the forecast is, the less funds are needed to be tied up to the costly buffers.

Depending on the balance sheet of the bank, the IRRBB figures calculated based on a standardised framework, national regulation as well as the bank’s own assumptions might differ materially from each other. Being able to calculate all the various scenarios performed in parallel, without extending the calculation time, increases the risk awareness and understanding of the differences between the various scenarios.

Find out more about MORS Data Aggregation and MORS IRR Scenario Engine.

Discover how banks are preparing for IRRBB in MORS IRR MANAGEMENT SURVEY.