Improving productivity and efficiency through a multistage

implementation Financial services firms can take an existing inefficient infrastructure

for risk management and compliance and gradually grow it into an integrated,

highly efficient grid system.

As shown, an existing infrastructure may comprise stove

pipes of legacy applications disparate islands of applications, tools

and compute and storage resources with little to no communication among

them. A firm can start by enabling one application a simulation

application

for credit risk modeling, for example to run faster by using grid

middleware to virtualize the compute and storage resources supporting

that application.

The firm can extend the same solution to another application, for

example,

a simulation application used to model market risk. Compute and storage

resources for both simulation applications are virtualized by

extending the layer of grid middleware; thus both applications can

share processing power, networked storage and centralized scheduling.

Resiliency is achieved at the application level through failover built

into the DataSynapse GridServer. If failure occurs or the need to prioritize

particular analyses arises, one application can pull unutilized resources that are

supporting the other application. This process also facilitates

communication and collaboration across functional areas and applications to provide

a better view of enterprise risk exposure.

Alternatively, a firm can modernize by grid-enabling a particular

decision engine. A decision engine, such as one developed with Fair Isaac’s

tools, can deliver the agility of business rules and the power of predictive

analytic models while leveraging the power of the grid to execute decisions

in record time. This approach guarantees that only the computeintensive

components are gridenabled while simultaneously migrating

these components to technology specifically designed for decision

components.

Over time, all applications can become completely grid-enabled or

can share a common set of gridenabled decision engines. All compute

and data resources become one large resource pool for all the

applications, increasing the average utilization rate of compute resources

from 2 to 50 percent in a heterogeneous architecture to over 90 percent

in a grid architecture .

Based on priorities and rules,DataSynapse GridServer automatically

matches application requests with available resources in the distributed

infrastructure. This real-time brokering of requests with available

resources enables applications to be immediately serviced, driving

greater throughput. Application workloads can be serviced in task units of

milliseconds, thus allowing applications with run times in seconds to execute

in a mere fraction of a second. This run-time reduction is crucial as

banks move from online to real-time processing, which is required for

functions such as credit decisions made at the point of trade execution. Additionally, the run time of

applications that require hours to process, such as end-of-day process and loss

reports on a credit portfolio, can be reduced to minutes by leveraging

this throughput and resource allocation strategy.