Making sources of risk transparent

Best practice risk management dictates that risk professionals within a banking organisation are able to quickly and accurately identify sources of risk, and effectively report these upwards to senior management. Corporate governance regulations have increased the visibility of the risk function with many CROs now reporting directly to the CEO. Mat Newman, director of product management for Adaptiv, discusses how to determine the correct level of systems support for such risk reporting

Data completeness

The basis for any sound risk reporting system is a comprehensive and accurate set of data pertaining to the risks under review. For credit risk this means generating a single obligor view of counterparty exposures, along with important customer characteristics, which are typically used for risk aggregation purposes - industry, rating, geography etc. Gathering this data from multiple disparate source systems can be a daunting task. Interfaces need to be built and maintained between the risk system and trading systems, market data feeds, customer master files, legal agreement repositories and so forth. It is not uncommon to have 20¨C30 different systems providing key data into this process. With so much data coming from so many systems, robust auditing and control along with built-in fault tolerance is required. As well as traditional reconciliation processes to assess data quality, practical considerations such as late arrival of incoming feeds need to be planned for, with appropriate contingencies in place.
Not only must the risk data be correct, but Basel II mandates that it is verifiably consistent with data used to make operational decisions. Thus tracking what was changed when, by whom (or by which external system) and when it came into operational effect are important aspects of the data problem.

Risk calculations and results distribution

Once the data is assembled the appropriate risk analyses may be run.
Whether these analyses are simple aggregations or more complex Monte-Carlo simulations, it is important that the methodologies and input assumptions are clearly defined and auditable. Typically, there will be multiple risk calculations run, each highlighting different aspects of the firm's overall risk profile. Clarity and appropriateness are required in presenting these results to the intended audience: a trader undertaking a pre-deal enquiry needs a quick unambiguous "yes/no" response, not pages of detailed exposure profiles and drill-down analyses. A risk controller investigating a limit breach on the other hand needs to know not only what the dealer was told at the moment of deal-entry but what additional relevant activities occurred at that time, such as other trading activity, limit changes, or changes to static data.

A risk system must be capable of giving users information, which is relevant to their needs in a timely and controlled manner. Sometimes the most appropriate data set may be an end-of-day snapshot or some other periodic reporting cycle. Other times it is vital to get up to the second real-time information. What is important is that the information relevant to business decision making is available to those professionals in the bank at the time they need it. Cutting down on excess administrative tasks such as sourcing, cleaning and interpreting data can be one of the biggest sources of payback from a modern risk system. Replacing outdated manual processes where information is gathered from several systems and compiled into spreadsheets should be high on the priority list for risk managers.

SunGard Adaptiv Credit

Adaptiv Credit from SunGard is an enterprise-wide counterparty credit exposure measurement, management, and limit control solution, which is designed to ease the collection, aggregation, and reporting of important risk information. Key aspects of the solution include:

· Supporting a flexible data model and componentised calculation framework. The system computes different risk calculations in parallel with multiple aggregation dimensions. It adapts to every bank's data standards and systems infrastructure, easing the implementation and integration process, reducing project delivery risk, and lowering total cost of ownership.

· Using web technologies, which means results are available to users across the bank in real-time enabling better, faster and more informed risk decisions to be made. Provision of both high-level overviews and detailed drill-downs tailors the output to each user's unique requirements and preferences.

· Adaptiv Credit supports Active Risk Management with the provision of data-driven workflow for key credit processes. Routine tasks such as limit breach investigation are speeded up by ensuring that all relevant data is easily accessible, thereby reducing operational overheads.

CONTACT

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