One obstacle to better risk management is data latency. In today's business environment where initiatives such as straight-through processing and online trading have become mainstream, risk levels for an organization can change in a matter of minutes. A significant amount of time is required to gather, compute and provide a consolidated view of information from trading data, portfolios, market information and financial systems. By the time all of the data has been processed, the usefulness of the data may be lost.
Other data issues complicating the task of monitoring and managing risk include:
- Data and events may come from many disparate sources.
- Data types and formats can vary from one source to the next.
- Large volumes of events and data must be processed.
- Events must be synchronized according to time of occurrence.
- Data and events come from a combination of real-time feeds and batch processes.
As if that weren't enough, with the increased complexity and speed of today's financial systems coupled with the geopolitical uncertainties around the world, financial institutions now face increased exposure to operational risk.
In a bid to prevent operational risk from undermining the stability of major financial institutions, the Bank for International Settlements (BIS) has spearheaded moves to compel banks to deal with this area of risk. In January 2001, the Basel Committee of Banking Supervision of BIS issued a collection of documents known as Basel II that extended the provisions of the original Basel Accord on operational risk. As a result of Basel II, financial institutions must face the new challenge of how to consistently measure risk exposure in order to reduce reserve requirements.
Operational risk is the least understood and hardest to measure of all risk types facing financial institutions and poses the biggest challenge in terms of managing with latent data. Operational risk can lurk in a wide range of potentially damaging events, from unauthorized trading and embezzlement to natural disasters and terrorism. To properly monitor operational risk, firms must take into account the entire scope of business activity, including every transaction. They must define and track multiple key risk indicators (KRIs) in real time. As KRI exceptions occur, alerts must be sent out quickly so that corrective action can be taken and losses can be minimized. And unlike market risk and credit risk, which involve only risks associated with trading and lending, everyone in the organization can be a source of operational risk.
Banks that can, however, monitor and measure all types of risk more closely are able to minimize the amount of reserve capital they are required to put aside, thereby enabling them to put more capital to work and ultimately become more profitable. This is why some companies have begun to take a new and fundamentally different approach to monitoring risk through operational business intelligence. Instead of focusing on what has happened (with latent data) and trying to predict what will happen next, these companies focus on what is currently happening in the business. This new approach to business intelligence is called business activity monitoring (BAM).
What is BAM?
A term originally coined by Gartner, BAM is, simply put, a technology to gain meaningful, instant visibility into enterprise operations. It works by capturing events from operational systems, correlating these events with relevant contextual data and delivering critical business information to decision-makers.
In a BAM system, front-line risk managers use a "dashboard" of key performance information, updated continuously with streaming/real-time data, to monitor and manage the key risk indicators and key performance indicators (KPIs) that characterize critical aspects of business performance. This dashboard is also a mechanism for analysts to define the business rules underlying the system. Since BAM is a real-time process, risk managers working with a true BAM system are able to dynamically model new rules and KPIs as the business environment changes, usually without IT intervention.
The ideal BAM software architecture is able to:
- Integrate with an operational dashboard to deliver continuous alerts and metrics based on the underlying data;
- Capture event-driven real-time data from a wide variety of sources: from message queues, Web services and databases to context-oriented sources such as data warehouses and traditional databases;
- Calculate temporal information by collating time-series data sets from event streams, such information can then be used to trigger time-based thresholds;
- Perform dynamic modeling by integrating and correlating event and contextual information on the fly and producing a stream of analytical models - models that can automatically update themselves based on input from subsequent event or contextual information;
- Execute business rules to set thresholds according to key performance indicators and other business-specific triggers; and
- Deliver alerts, metrics and reports into any workflow or case management environment, including e-mail systems, RDBMS, Web services, middleware and custom applications.
