The importance of risk management has always been clear in financial services, but it is growing in other industries, including energy and telecommunication companies. This importance is being amplified through increasing demand for risk and finance integration.
This also has implications for the underlying data and computational architecture. There is increased convergence between data and computing environments. Data architecture will increasingly need to co-exist with computing components, providing dynamic aggregation and integration capabilities. This leads to a broader footprint and coverage of risk operations. Systems are increasingly open and exposed to third parties through prime brokerage and securities services divisions.
Innovation in risk has often come from firms with complex trading books. This may also be the case with risk and finance integration: these firms recognise the need to use analytical insights from a combined risk/finance dataset to stay ahead of the competition. They have established advanced approaches to managing credit risk, and understand that a silo mentality is limiting their understanding of a firm’s performance. They can also see where to make changes to improve efficiency and reduce costs.
The driver for consolidating risk systems is often the integration of regulatory charges and a multidimensional view of risk. Risk data takes centre stage when considering regulatory risk. New risk data architecture is needed to focus on the integration of time series data, and finance and regulatory reporting is increasingly moving towards a model-free framework.
These internal consolidation strategies will drive initiatives
This has wide-ranging effects, for example on data management and technology. Closer integration of risk and finance means that banks will be looking for an advanced framework that integrates, validates and transforms multiple data feeds from several sources. Regulations require banks to implement a comprehensive data model that integrates the components of risk systems. This can potentially also capture the risk factor data sets. A consolidated P&L platform can bring together pricing, valuations and a framework for explaining and simulating the P&L. This single, consolidated platform will be focused on risk and performance analytics based on enterprise reference data.
This has knock-on effects for risk analytics, such as the use of Monte Carlo simulations. A powerful engine is needed, to simulate over 1000 risk factors using correlated random numbers. It must also be seamlessly integrated into the risk management process. Other requirements include economic capital calculations for proprietary option books of interest rates, foreign exchange, precious metals, and commodities, sensitivity analysis, scenario generation, exposure calculation, balance sheet optimization, underwriting risk and behavioral analytics. These risk and finance analytics also require skilled resources back-testing models, calibrating and providing extensive risk data analysis.
What about disclosure, financial reporting and governance? Banks will want a common reporting architecture for internal and regulatory requirements, following a factory approach and having easy-to-customize features. Governance is a key aspect of the development, support and maintenance of risk applications for all business areas. The starting point from a business perspective, however, usually remains credit risk.
Risk management efficiency can be a competitive advantage. A reliable and integrated view for managerial purposes requires trust across the organisation through a proactive and systematic management of risk exposures. Join our upcoming webinar to discover the better path to Governance, Risk and Compliance
Credit is the foundation of a wide variety of applications
Credit analytics covers a number of sub-topics, including credit portfolio management, fraud analytics, and credit trading. Credit risk is therefore part of:
- Application processes with pricing, calculation of limits and credit portfolio management;
- Transaction processing, with fraud analytics and real-time credit checks;
- Enterprise risk, with risk aggregation and counterparty credit risk; and
- The finance department, through risk-aware accounting, product control, valuations and performance analytics.What does this mean in practice? What does the changing face of credit look like? There are plenty of new institutions, such as FinTechs, getting into the market space. This has led to increasing demand for real-time analytics and payments systems based on micro-transactions. This, in turn, creates demand for completely new types of analytics, leveraging new data.
Enabling better integration choices
It no longer makes sense to approach risk and finance individually. Closer integration of risk and finance creates the rocket fuel powering the transformation of credit risk. There will, of course, be plenty of challenges in the emerging risk and finance landscape, along with a number of new opportunities. There is likely to be more risk-aware finance, risk data increasingly used in finance and an expanded role for performance metrics, as well as the emergence of enterprise performance frameworks.
They say that a problem shared is a problem halved. SAS communities have helped to bring our global customers together to share experience and solutions on a wide range of issues. We have therefore just established a new closed SAS community on this new area of risk and finance analytics. Invited customers from around the world can now share their experience and discuss related business issues. The group is called Risk & Finance Analytics and you can find it at communities.sas.com.
