Financial institutions rely heavily on quantitative models for risk management, balance-sheet stress testing and various business analyses and decision support functions. Investment decisions and business strategies are largely driven by estimates from models. Recent financial crises and model failures at high-profile banks have emphasized the need for better modeling practices. Regulators have stepped-in to assist banks with enhanced guidance and regulations for effective model risk management. Effective model risk management is more than developing a good model. SAS^® Model Risk Management provides a robust framework to capture and track model inventory. In this paper we present best practices in model risk management learned from implementation projects and interactions with industry experts. These best practices help firms that are setting up a model risk management framework or enhancing their existing practices.

The recent advances in regulatory stress testing, including stress testing regulated by Comprehensive Capital Analysis and Review (CCAR) in the US, the Prudential Regulation Authority (PRA) in the UK, and the European Banking Authority in the EU, as well as the new international accounting requirement known as IFRS 9 (International Financial Reporting Standard), all pose new challenges to credit risk modeling. The increasing sophistication of the models that are supposed to cover all the material risks in the underlying assets in various economic scenarios makes models harder to implement. Banks are spending a lot of resources on the model implementation but are still facing issues due to long time to deployment and disconnection between the model development and implementation teams. Models are also required at a more granular level, in many cases, down to the trade and account levels. Efficient model execution becomes valuable for banks to get timely response to the analysis requests. At the same time, models are subject to more stringent internal and external scrutiny. This paper introduces a suite of risk modeling solutions from credit risk modeling leader SAS^® to help banks overcome these new challenges and be competent to meet the regulatory requirements.

There are standard risk metrics financial institutions use to assess the risk of a portfolio. These include well known measures like value at risk and expected shortfall and related measures like contribution value at risk. While there are industry-standard approaches for calculating these measures, it is often the case that financial institutions have their own methodologies. Further, financial institutions write their own measures, in addition to the common risk measures. SAS^® High-Performance Risk comes equipped with over 20 risk measures that use standard methodology, but the product also allows customers to define their own risk measures. These user-defined statistics are treated the same way as the built-in measures, but the logic is specified by the customer. This paper leads the user through the creation of custom risk metrics using the HPRISK procedure.

We introduce age-period-cohort (APC) models, which analyze data in which performance is measured by age of an account, account open date, and performance date. We demonstrate this flexible technique with an example from a recent study that seeks to explain the root causes of the US mortgage crisis. In addition, we show how APC models can predict website usage, retail store sales, salesperson performance, and employee attrition. We even present an example in which APC was applied to a database of tree rings to reveal climate variation in the southwestern United States.

In the aftermath of the 2008 global financial crisis, banks had to improve their data risk aggregation in order to effectively identify and manage their credit exposures and credit risk, create early warning signs, and improve the ability of risk managers to challenge the business and independently assess and address evolving changes in credit risk. My presentation focuses on using SAS^® Credit Risk Dashboard to achieve all of the above. Clearly, you can use my method and principles of building a credit risk dashboard to build other dashboards for other types of risks as well (market, operational, liquidity, compliance, reputation, etc.). In addition, because every bank must integrate the various risks with a holistic view, each of the risk dashboards can be the foundation for building an effective enterprise risk management (ERM) dashboard that takes into account correlation of risks, risk tolerance, risk appetite, breaches of limits, capital allocation, risk-adjusted return on capital (RAROC), and so on. This will support the actions of top management so that the bank can meet shareholder expectations in the long term.

In Brazil, almost 70% of all loans are made based on pre-approved limits, which are established by the bank. Sicredi wanted to improve the number of loans granted through those limits. In addition, Sicredi wanted an application that focuses on the business user; one that enables business users to change system behavior with little or no IT involvement. The new system will be used in three major areas: - In the registration of a new client for whom Sicredi does not have a history. - Upon request by business users, after the customer already has a relationship with Sicredi, without customer request. - In the loan approval process, when a limit has not yet been set for the customer. The limit system will try to measure a limit for the customer based on the loan request, before sending the loan to the human approval system. Due to the impact of these changes, we turned the project into a program, and then split that program into three projects. The first project, which we have already finished, aimed to select an application that meets our requirements, and then to develop the credit measurement for the registration phase. SAS Real-Time Decision Manager was selected because it fulfills our requirements, especially those that pertain to business user operation. A drag-and-drop interface makes all the technical rules more comprehensible to the business user. So far, four months after releasing the project for implementation by the bank's branches, we have achieved more the USD 20 million granted in pre-approved loan limits. In addition, we have reduced the process time for limit measurement in the branches by 84%. The branches can follow their results and goals through reports developed in SAS Visual Analytics.