Introduction
Risk management today is top of mind for banking professionals around the world, and it has begun to revolutionize the way banks operate. No longer is risk management the sole responsibility of the chief risk officer or a bank's risk department; its responsibility now resides with vice presidents, associates, directors and managing directors across various business units.
Banks have felt the pressure more than other financial institutions to improve their risk management practices to avoid a repeat of the credit crisis. The need for improved risk management is not only being driven by regulators, but by internal and external stakeholders: investors, boards of directors and, in some cases, governments. Compliance aside, implementing effective risk policies that are designed to continuously manage a firm's risk-and-return profile and capital are required for the long-term success of a bank.
The recent distress in financial markets has placed greater emphasis on a financial institution's ability to demonstrate a comprehensive approach to viewing firmwide exposures and risk. A number of published papers point to the deficiencies of many financial institutions' risk management practices and the concrete actions that need to be taken. The Institute of International Finance report stated that a firm's risk management approach should not rely on a single risk methodology, but instead take a comprehensive approach, integrating strands such as credit, market, operational, liquidity and reputational risks. Rating agencies are increasingly focusing on the quality of a firm's enterprise risk management practices in their rating processes. For example, in 2008, Standard & Poor's announced that it will review the quality of enterprise risk management as a new component in its reviews of credit ratings.
An additional area of focus for banks is the need to provide transparency in the derivatives transactions and valuations that are being delivered. This change requires banks to step away from their closed, black-box systems and move toward third-party solutions offered by vendors – especially those that include comprehensive documentation of the models and methodologies used. Financial models developed in-house no longer provide the required level of transparency and independence needed to meet all stakeholder and regulatory requirements. In a 2009 report, the analyst firm Celent stated that disclosure and pricing or valuation methodologies were key starting points for restoring confidence in the derivatives and structured finance markets.
Banks are now taking a closer look at how they price and value derivatives. They are seeking better ways of assessing and measuring risk across the enterprise. And more importantly, they are seeking ways to integrate the valuations of their large and often disparate derivatives portfolios with an enterprise risk system that can handle large volumes of data. To save time and money from in-house development, banks look to best-of-breed, third-party solutions that can be easily integrated, while providing the capabilities they need.
Building an Integrated Rated Risk Infrastructure
SAS, the leader in enterprise risk management solutions, and FINCAD, a leading provider of derivatives analytics, have partnered together to offer a joint solution that will give firms:
- Comprehensive risk capabilities covering the spectrum of market, credit, operational and firmwide risk.
- Access to one of the broadest cross-asset class derivatives and fixed income analytics library that uses industry-standard models.
- Full disclosure of the data, models, analytics and risk measures used.
- Assurance knowing they are working with proven and accepted solutions from two vendors that are leaders in their respective fields.
SAS supports financial institutions' risk management activities by delivering functionality for all major risk types, as well as data management and reporting. It is important that the risk management solution allows business units to independently and separately calculate measures of risk, such as market, credit and ALM, as well as calculate firmwide risk measures using models and correlated aggregation techniques.
FINCAD provides financial institutions with independent valuations of their securities portfolios for all major asset classes, including interest rate, foreign exchange, commodity, credit and equity derivatives, mortgage-backed securities, fixed income securities and structured products.
Key Requirements
The challenges faced by financial institutions demand these key requirements:
- A quality integrated risk data infrastructure with timely access to data.
- The ability to measure exposures and risks across all risk types and books of business for better control.
- The ability to distribute incentives for consistent optimization of risk-adjusted returns throughout the organization.
SAS® meets these requirements through an architecture that supports the data requirements, methodology requirements, usability criteria and ability to distribute key risk information effectively across the enterprise for many different users. SAS provides functionality that allows users to integrate FINCAD's derivatives analytics library directly into the system for analysis.
A key requirement of a risk management system is the ability to support several different risk application streams within one common environment. Business units need specific risk calculations and monitoring capabilities. At the higher levels of the organization, these risks will need to be integrated and aggregated to create firmwide measures.
The integrated SAS solution, SAS Risk Management for Banking, covers the four main risk areas:
- Market risk
- Credit risk
- Asset and liability management
- Firmwide risk
The applications are based on a common data model with predefined extraction, transformation and loading (ETL), risk analytics and risk reporting. The integrated risk applications in SAS Risk Management for Banking enable users to get up and running quickly, while the open infrastructure of the solution allows users to support not only current business requirements, but also future requirements on data and risk analytics.
Key Components
The key components for building an integrated risk infrastructure include:
- Data management.
- A powerful risk engine.
- Independent and transparent valuations.
- Reporting and workflow configuration.
Data Management
Regulators have pointed to incomplete, inconsistent and unreliable data as contributing to the financial crisis, and continuing inadequacies in this area are a major obstacle to more robust and efficient markets. Disparate data sources form an often overwhelming hurdle for anyone involved in the implementation of an ERM solution. Analyst firm TowerGroup3 stated that more than 50 percent of the effort in implementing an enterprise wide risk management system is feeding data.
Challenges include everything from the sheer number of sources to ensuring the integrity of the staged data. The process of systematically organizing information to support an enterprise risk initiative is fundamental to ensuring good risk management practices. A well-designed approach to managing risk information is a critical component of a best-practice risk management program. However, high-quality risk information management programs continue to elude many organizations, and inferior information quality adversely affects risk management. It is important for organizations to understand not only how to assess the integrity of their risk data but also to know how to address this problem in a cost-efficient manner. Analysis of risk data availability and integrity needs to be addressed in a continuous, structured, methodical way, with measurements in place to feed analytics just like any other operational risk.
- Profile, monitor and actively manage the quality of enterprise risk data:
- Analyze and assess the reliability, accuracy and general state of data across the risk management spectrum to determine where potential problems exist and what efforts will be required to rectify them BEFORE projects start.
- Focus on root-cause analysis to improve key risk data areas and processes for better planning and project execution.
- Integrate and standardize risk data from multiple systems and business units into a unified, accurate view:
- Implement both standardized and customized risk data processes across the enterprise with consistent business rules.
- Adopt consistent data quality business rules across risk and other data sources and platforms.
- Ensure regulatory compliance with state-of-the-art data quality technologies that enable business and technical users to cleanse, standardize, integrate, match and augment risk data using a specialized interface and state-of-the-art data quality tools that can be customized to meet organizational requirements:
- Define risk data correction rules to reflect organizational changes and adjust data where needed.
- Visualize the impact of business rules and data cleansing efforts.
- Provide decision makers with trustworthy information for managing risk:
- Automatically incorporate consistency, accuracy and business rule checks on your data into your risk processes or infrastructure.
- Deliver consistent information. Reports and reliable analytic results are based on risk data that is both accurate and current.
Organizations are becoming keenly aware of the ramifications of unreliable, inconsistent poor data: dissatisfied customers, confused employees, unhappy stakeholders and poor bottom-line results.
A Powerful Risk Engine
SAS® Risk Dimensions® is the powerful and versatile risk engine underlying the analytical functionality of SAS Risk Management for Banking. The SAS risk engine supports a wide range of risk analysis methods and is the preferred solution for the quantitative risk analyst and model builder. While SAS Risk Management for Banking has both standard and advanced risk analyses preconfigured, as well as pricing in the risk engine, the risk engine is also designed to support a flexible development framework. This flexible development framework includes the ability to configure proprietary risk factor models, as well as user-defined and external pricing libraries and cash flow models.
Independent and Transparent Valuations
FINCAD's analytics library feeds into the SAS risk engine, providing independent valuations of a wide range of derivatives and fixed income securities. Independent valuation is a key requirement in today's marketplace. Recent regulatory changes and the need to ensure long-term viability are driving the demand. Leveraging third-party solutions like FINCAD Analytics can greatly benefit a financial institution by offering cost efficiency, scalability and flexibility. FINCAD is a derivatives analytics specialist that is focused exclusively on keeping its analytics solutions current with the changing world of analytic finance.
An implementation of any system is not an easy task. Likewise, the need for an analytics vendor requires an examination of your current and future needs. Selecting a vendor that offers cross-asset class analytics and a proven track record is ideal. FINCAD Analytics coverage is extensive and wide-ranging, covering all major asset classes:
| Asset Class | Instrument Range |
| Interest Rates | From vanilla swaps to CMS spread swaptions. |
| Foreign Exchange | From European options to power reverse dual currency (PRDC) notes. |
| Credit | From single asset CDS to complex collateralized debt obligations (CDOs). |
| Fixed Income | From noncallable government debt to range accrual notes. |
| Equity | From warrants to callable/puttable convertible bonds. |
| Commodity | From index options to commodity swaps. |
| Mortgage-Backed Securities | From fixed-rate passthroughs to collateralized mortgage obligations. |
| Volatility | From volatility swaps to conditional variance swaps. |
To support the hundreds of instrument types a bank may have in its portfolio, both simple and complex models are required. FINCAD Analytics uses a variety of models including, but not limited to:
- Black and Black-Scholes.
- Various stochastic volatility (Heston and SABR).
- LIBOR Market Model.
- Gaussian copula.
FINCAD Analytics is a dynamic derivatives library that keeps pace with the changing derivatives market. Additional instrument coverage, new models and new asset classes get added to the library as the market evolves. FINCAD recently included the ISDA CDS model in its latest release of the analytics library.
To help mitigate systemic risk, financial institutions are moving toward best practices that incorporate more transparent processes for derivatives valuations. Black-box solutions are no longer acceptable from a transparency and risk control perspective. Financial institutions are being asked to disclose all the facts about their derivatives transactions:
- What are the models and methods used to value derivatives?
- How accurate are the inputs to the valuations?
- What risks are involved and how are they measured?
- Who are the counterparties involved and what is their level of risk?
All FINCAD solutions contain comprehensive documentation where every model, calculation methodology and reference is fully documented, providing the transparency needed to understand how the models work and how they are implemented. FINCAD documentation is granular to the point that it shows the mathematics behind the functions used to value instruments and also how the curves are constructed. Instead of proprietary models, FINCAD uses industry standard analytics models that are published and in the public domain – giving organizations access to published papers discussing the model, its uses and limitations. The valuations provided by FINCAD Analytics are fed into the SAS risk engine for further risk analysis.
Reporting and Workflow Configuration
SAS Risk Management for Banking is part of the SAS Business Analytics Framework, which combines advanced data integration, analytics and reporting capabilities. With this framework, users get the information they need, when they need it, in their preferred format. The SAS Business Analytics Framework also offers a robust and flexible presentation layer for the full breadth of SAS Analytics capabilities – all integrated within a business context for better, faster decision making. Using SAS Stored Processes, users can configure their own workflows and integrate daily and ad hoc, advanced risk analytics procedures into their preferred environments (e.g., using the SAS Add-In for Microsoft Office, users can integrate their reporting and analysis workflows into their desktop environments).
SAS Risk Management for Banking comes with a wide array of preconfigured reporting and risk analysis workflows. The report framework includes sample reports, OLAP cubes and interactive analysis results for all the application components of SAS Risk Management for Banking.
As the bank creates risk measures, employees may quickly find that bringing risk information together to support enterprise wide reporting is also very challenging. To meet this challenge, SAS Risk Management for Banking provides a common reporting data model. This data model – the SAS Risk Reporting Repository – supports the integration and reporting of enterprise risk measures as well as decomposed measures at the entity, business unit, geography or any other user-defined hierarchy level. This repository provides the audit, change, archive and historization support required by rigorous reporting requirements. The SAS Risk Reporting Repository allows the bank to meet both current and future reporting requirements, while exploiting the power of the SAS Business Analytics Framework.
A Joint Solution from Leading Vendors
As banks increasingly look to best-of-breed, third-party solutions for their risk management and independent derivatives valuation needs, SAS and FINCAD have partnered to offer a joint solution to address these needs. Following this best-of-breed approach can save a bank significant time and costs over developing the systems in-house.
SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions delivered within an integrated framework, SAS helps customers at more than 45,000 sites improve performance and deliver value by making better decisions faster. SAS' comprehensive risk offerings are built on the SAS Business Analytics Framework, a powerful blend of data integration, analytics and reporting capabilities. This leading position was recognized in Chartis' 2009 RiskTech100 report, where SAS received the No. 1 overall position.
FINCAD has been providing software and services supporting the valuation and risk management of cross-asset class derivatives and fixed income securities for close to 20 years. FINCAD solutions are used by more than 35,000 financial professionals in over 80 countries and have become the industry standard for financial analytics. FINCAD was recognized as a leading vendor in Celent's 2009 report, Pricing Solutions for OTC Derivatives and Structured Products.
The joint solution from SAS and FINCAD is an enterprise risk management system with independent derivatives valuations. The joint solution has already been successfully implemented at leading financial institutions, including one of the top 10 largest banks in the US. If your organization is interested in learning more about the joint solution, contact a representative from FINCAD.