Transforming raw business data into meaningful insights remains a continuous challenge for organizations. Since information is scattered all over the organizational IT landscape, businesses are forced to rely on manual reconciliation of data from various systems, users, and reports. Today, organizations increasingly need applications that combine data from various parts of the business, as well as tools to convert that data into formats such as data visualizations, charts, and reports for business users.
While it is crucial to ensure the integrity of data provided to executive management and regulators, unlocking the insights in the data to better understand customers, competitors and employees represents a significant opportunity to gain competitive advantage. While regulatory pressure is forcing organizations to improve the integrity of the data, many financial institutions are seeing improved data quality and the use of analytics as an opportunity to fundamentally change the way decisions are made and to use the data for commercial gain.
Various Types of Analytics
Today, global organizations operate in an environment marked by growing uncertainty in business outlooks, customer centric regulations, stricter regulatory supervision, and rising costs of services. External challenges and the economic recession have brought along an increased probability of operational lapses and internal fraud. Reduced margins and target pressures often result in client-centric operational risks such as poor product pricing, mis-selling, and inadequate suitability checks. Dealing with all these challenges requires an effective operational risk framework to manage uncertainties and operational risks.
- Business Intelligence and Analytics Functionality
- Operational Risk Analytics
- R-Analytics – A statistical Modeling Tool for Operational Risk Management
Financial Services companies, more than ever before, are making headlines in the media due to financial frauds, massive losses due to rogue traders, or non-compliance with regulatory requirements. Financial products are becoming more complex, regula¬tion more stringent and customers more demanding. All this puts pressure on the bank’s processes and controls that are the foundation of Operational Risk Manage¬ment. Large operational losses, especially those that ultimately result in Reputation Risk, can have immense damaging potential.
VirtusaPolaris Offers Solution to Improve Analytics
Recognizing this rapidly evolving and continuous need, VirtusaPolaris in partnership with MetricStream provides a range of basic and advanced reporting functionalities as a part of its GRC solution. MetricStream’s GRC Platform, which is the underlying enabling entity for the solution, contains an embedded reporting engine that enables users to generate various types of dynamic reports and data visualization charts for business analysis. The platform contains pre-integrated third-party tools, as well as advanced capabilities such as an easy-to-use reporting wizard. The platform is also flexible in integrating with enterprise third-party reporting tools to generate different types of reports according to business needs.
As Operational Risk Management (ORM) takes center stage in the enterprise risk frameworks of organizations, there is an increasing focus on ORM analytics. VirtusaPolaris in partnership with MetricStream provides R Analytics - a leading statistical modeling tool that is part of MetricStream’s ORM Solution, and enables banks to manage operational risk both qualitatively and quantitatively. The tool includes risk analytics in the form of risk heat maps, charts, trend analyses, key risk indicators, Value-at-Risk (VaR) models, and what-if scenario analysis tools.
Key Advantages of our framework
- Uses Monte Carlo simulation techniques for modeling internal and external losses at 99.99% confidence level
- Enables back testing of VaR results
- Enables calculations of operational risk capital which can be used for efficient risk-based capital allocation
- Improves transparency of risk modeling parameters, assumptions, and techniques to support local regulator audits
- Makes available comprehensive VaR model documentation for future updates
- Delivers insurance cover adjustments in accordance with Basel II
- Helps banks specify their own loss distributions, business lines, and risk types as per the Loss Distribution Approach (LDA)
- Supports severity distribution models, including log normal, Pareto, and Burr
- Provides a fully parameterized modeling architecture to adapt to each bank’s unique requirements; supports internal audits and independent verification of modeling results (Basel II requirement)
VirtusaPolaris' Solution to Analytics Offers the Following Benefits
- Significantly faster Identifications and Sourcing of data elements
- Delivers integrated tools for reporting, dash-boarding, analysis, and data integration
- Enables the development of a flexible drag-and-drop based reporting layout
- Helps generate and embed dynamic content into reports in an intuitive manner
- Supports an extensive array of chart types through built-in comprehensive charting capabilities
- Provides flexible report output options
- Enables dynamic charting
- Contains a plethora of chart types
- Allows users to export charts as images, or in CSV or PDF formats
- Supports unlimited drill down levels