Financial institutions continue to expand their use of statistical models across the enterprise. In addition to their long-standing role in risk management, models are increasingly the foundation for customer insight and marketing, financial crime and compliance and enterprise performance management analytical applications.
As a result, organizations are spending more time and resources creating and validating models, improving data quality, verifying results and managing and governing the use of models across the enterprise. To help financial institutions meet new requirements, accelerate model development and deployment and reduce model management complexity, Oracle Financial Services introduced Oracle Financial Services Analytical Applications R Modeling Framework as a new component for Oracle Financial Services Analytical Applications Infrastructure.
Oracle Financial Services introduced Oracle Financial Services Analytical Applications R Modeling Framework, a new metadata driven, enterprise-modeling framework, as part of the Oracle Financial Services Analytical Applications Infrastructure. The R modeling framework extends Oracle Financial Services Analytical Applications' metadata driven approach to analytics, to statistical modeling in R.
With this new framework, financial institutions create complete analytical applications by easily integrating statistical models exposed as services with deterministic business logic processes, all within the same platform.
Treating models as metadata objects allows the framework to audit and trace data as it moves through the analytics workflow from data integration to statistical modeling to reporting.
The solution brings the power and flexibility of the open source R statistical platform, delivered via the in-database Oracle R Enterprise engine that supports open standards compliance, and adds the enterprise model management and governance capabilities today's financial services institutions require.
It offers several important productivity, management and governance benefits to financial institutions, including the ability to:
o Centrally manage and control models in a single, enterprise model repository, allowing for consistent management and application of security and IT governance policies across enterprise assets.
o Reuse models and rapidly integrate with applications by exposing models as services.
o Accelerate development with seeded models and common modeling and statistical techniques available out-of-the-box.
o Cut risk and speed model deployment by testing and tuning models with production data while working within a safe sandbox.
o Support compliance with regulatory requirements by carrying out comprehensive stress testing, which captures the effects of adverse risk events that are not estimated by standard statistical and business models. This approach supplements the modeling process and supports compliance with the Pillar I and the Internal Capital Adequacy Assessment Process stress testing requirements of the Basel II Accord.
o Improve performance by deploying and running models co-resident with data. Oracle R Enterprise engines run in database, virtually eliminating the need to move data to and from client machines, thereby reducing latency and improving security.
"As model complexity and regulatory scrutiny increases, organizations are looking for new ways to effectively manage and optimize their use across the enterprise," said Sultan Khan, group vice president, Oracle Financial Services Analytical Applications. "Oracle Financial Services Analytical Applications R Modeling Framework gives financial institutions new power to perform in-warehouse analytics and integrate model management into broader risk management initiatives, increase transparency and improve overall model governance and control. Part of the Oracle Financial Services Analytical Applications suite, the new framework is poised to bring new precision and governance to modeling initiatives."