STRESS AND STRATEGY: A C-SUITE GUIDE TO SCENARIO- BASED ...

STRESS AND STRATEGY: A C-SUITE GUIDE TO SCENARIO-BASED RISK MANAGEMENTMoving beyond compliance to unlock business value

2 I A C-SUITE GUIDE TO SCENARIO-BASED RISK MANAGEMENT

INTRODUCTIONWhile the current political environment suggests a relaxation of regulatory policy, bank executives should not take this as a sign that the risks they face are waning. Geopolitical issues, a changing interest rate environment and numerous other factors create the potential for the next downturn or financial crisis, and the C-suite must remain risk-aware and prepared to react quickly as events unfold. Here, scenario-based risk assessment remains vital in managing an institution’s risk profile and preparing for defensive action in a crisis.


Organizations must adjust their strategies to address disruption and market shifts and ensure they maintain appropriate controls and governance while doing so. Scenario-based risk management is a valuable tool to inform innovation and strategic decision-making — and it instills methods to actively monitor and mitigate changing risks.

This eBook will explore some of the ways the top-performing organizations are undertaking scenario-based risk assessment to develop and manage their business strategies:

� Lessons learned from the supervisory stress test process

� Bottom-line benefits of a systematic program for scenario-based risk management

� Strategies for successfully implementing a scenario-based risk management program

� Technological advancements enabling strategic scenario-based risk management


LESSONS LEARNED FROM SUPERVISORY STRESS TESTS Supervisory stress tests, such as the Federal Reserve’s test implemented as part of the Dodd-Frank Act, are designed to assess the potential micro- and macro-level impact of general stresses on the overall economy. Through these periodic exercises, regulators assess the ability of large institutions to weather a broad, severe economic downturn and ensure an acceptable risk-based regulatory capital level is maintained at each


institution. Supervisory stress testing has provided a common framework for monitoring systemic risk, managing regulatory capital levels at supervised institutions and ensuring the investment community that the financial system remains robust.

An early lesson learned from supervisory stress tests is that there is no one-size-fits-all risk model. To be effective indicators of an organization’s risk profile, scenario-based risk assessments need to be tailored to each specific organization’s risk profile.

“A scenario-based risk assessment must take into account the operating sensitivities of the organization,” said Bruce G. Stevenson, President and Executive Consultant, The Bruce G. Stevenson Company, LLC. “If the organization has a large number of commercial loans, for example, the scenario-based risk assessment must take into account the impact on bank performance should commercial real estate loans default in large numbers or outperform expectations.”

Another important lesson from the supervisory tests is that collecting and preparing data for scenario-based risk assessments is more challenging and time consuming than many financial institutions initially expected.


“It can be initially painful to access the data needed to ensure that risk assessment models present an accurate view of specific risks to the organization,” Stevenson said. Organizations often underestimate the challenge of validating and normalizing internal and external data to improve the accuracy of scenario-based models.

Other lessons learned are the need for banks to manage their finite internal resources while maintaining their ongoing business functions and the need for them to invest in technology for a level of automation. Timelines for Federal stress tests are known well in advance, yet many organizations still struggle to find the tools and people to meet the compliance burden. This has led leading institutions to seek technological solutions to reduce the resource demands of the exercises. Still, many organizations are not where they need to be if they seek to move stress testing beyond meeting simple compliance requirements.

“Computing power, operational processes, and developing an infrastructure that includes appropriate data and analytics capabilities, are some of the internal initiatives that organizations struggle with as they move toward scenario-based models for risk assessment,” said Tom Kimner, Director of Global Risk Marketing and Operations for analytics leader SAS.

Automating processes that have previously required human judgement is also a challenge, according to Paul Fagone, Partner, KPMG. “Expert judgement of risk professionals is difficult to replicate, and organizations struggle to come up with an appropriate validation framework.”

“EXPERT JUDGEMENT OF RISK PROFESSIONALS IS DIFFICULT TO REPLICATE, AND ORGANIZATIONS STRUGGLE TO COME UP WITH AN APPROPRIATE VALIDATION FRAMEWORK.”

— Paul Fagone, Partner, KPMG


PULLQUOTE FUGIT OPTATI OFFICIIS DUCILLAREM BOLD HIGHLIGHT SEQUE SUNT. SED UT PORRUM QUUNT OFFICKJNCC

HOW SCENARIO-BASED RISK MANAGEMENT BENEFITS PROFITABILITYA comprehensive scenario analysis program, which centralizes data and models and automates much of the workflow, can reduce costs—increasing overall profitability—by providing greatly improved process efficiencies, enabling a richer understanding of the portfolio risks, and supporting the quantitative assessment of potential mitigation efforts in a crisis.


Leveraging a common platform for scenario-based modeling enables organizations to improve overall efficiencies by encouraging centralization of data and models and operationalizing risk management processes. It provides a controlled and repeatable mechanism for assessing individual risks as well as quantifying the cumulative effect of multiple events. “Scenario modeling can create efficiency across the infrastructure, as everyone is using a common data and modeling platform that is tailored to the business, portfolio concentrations and expected environment,” Kimner said.

An efficient, automated scenario-based analysis process prepares the bank to quickly assess outcomes across multiple scenarios. This enables the institution to analyze a wider variety of assumptions and drill deeper into their implications to the portfolio. This helps organizations focus and prioritize mitigation efforts where they are most impactful and react quickly in the event of a crisis. “The goal is not to forecast the future, as no single scenario is going to provide forecasting accuracy, but having an ability to run a range of scenarios to assess potential outcomes under a variety of stresses will help management assess and mitigate risks and ensure the success of modern organizations,” says Kimner.

Scenario-based risk assessment results in a deeper understanding of potential issues and outcomes. More robust and nuanced scenario models enable organizations to evaluate how correlation of risk factors work


together to impact profitability and business models. For example, the effects of a prolonged natural disaster can be highly variant depending on the outlook for the underlying economic cycle.

Exploring the risk of portfolio concentrations through scenario-based risk management is critical to developing the bank’s portfolio strategy. “You may want to pull back in certain areas or emphasize other areas of the business as you rebalance your risk profile based on market conditions,” Kimner noted. “To provide a true assessment of risk, it is not sufficient to just look at where interest rates are headed or how geopolitical shocks will change the business, but how macro and market shifts translate to specific drivers of the underlying performance models and how whose performance drivers will impact cash flows and the bottom line.”

Scenario-based assessment can also facilitate operational planning, such as informing the allocation of customer service and collection resources in a crisis. Having an efficient well-oiled process in place provides management with a way to quickly assess potential moves and gives a strategic advantage over organizations that rely on a more instinctive approach.

“SCENARIO MODELING CAN CREATE EFFICIENCY ACROSS THE INFRASTRUCTURE, AS EVERYONE IS USING A COMMON DATA AND MODELING PLATFORM THAT IS TAILORED TO THE BUSINESS, PORTFOLIO CONCENTRATIONS AND EXPECTED ENVIRONMENT.”

— Tom Kimner, Director of Global Risk Marketing

and Operations, SAS


PULLQUOTE FUGIT OPTATI OFFICIIS DUCILLAREM BOLD HIGHLIGHT SEQUE SUNT. SED UT PORRUM QUUNT OFFICKJNCC

BEST PRACTICES FOR SCENARIO-BASED MODELING Implementation of a scenario-based analysis program is a journey, with short-, medium- and long-term benefits.

Stevenson noted that short-term objectives should be to assess the con-centration of assets on the balance sheet and to establish incentive mech-anisms for product specialists that reflect the institution’s risk appetite. As the program matures, medium-term goals include improving diversification of the balance sheet and gaining greater insights into decision-making, such as which customer to extend credit to, Stevenson said.


Longer-term, scenario-based risk management should support the setting of target capital levels to ensure investors that there is a low risk for potential loss. “That is critical to everything else,” Stevenson added.

To reach this level of capability, it is important to approach the implementation of scenario-based risk management incrementally. For example, while it is important to carefully manage data to ensure the model’s accuracy, too much data can add unnecessary complexity, making it difficult to know which variables are driving the outcome of a specific scenario. “The best advice is to start small with easily accessible data to build a foundation,” Kimner said.

Institutions must also consider the number and breadth of scenarios they will assess. Broad macroeconomic scenarios, such as those used in the supervisory stress test, can reveal an organization’s risk sensitivities to a general downturn, but it is important to consider scenarios to test market conditions that are more relevant to the business.

Scenarios must be maintained and revised to stay relevant. Kimner emphasized that scenarios should be reviewed at least annually, and particularly following a material event that would impact the relevance and effectiveness of the underlying model.

While a common set of scenarios facilitates trend analysis, institutions should avoid limiting themselves in this regard. “Repetitive use of the same scenarios provides an opportunity for organizations and people to become complacent,” Stevenson said. For example, the Fed highlighted this risk with the introduction of a negative interest rate scenario in its 2016 stress test, and some banks found that their models broke under such a scenario and they had to recalibrate.

“REPETITIVE USE OF THE SAME SCENARIOS PROVIDES AN OPPORTUNITY FOR ORGANIZATIONS AND PEOPLE TO BECOME COMPLACENT.”

— Bruce G. Stevenson, President and Executive

Consultant, The Bruce G. Stevenson Company, LLC


WHERE TECHNOLOGY CAN HELPSupervisory stress tests have presented an operational burden on banks. “These are computationally intensive exercises that have grown over time, requiring input from disparate systems and multiple data hierarchies,” said KPMG’s Fagone.

But through advances in technology, institutions can reduce the strain of production and elevate their assessment programs beyond compliance and achieve strategic value. Automation of data staging, modeling, and reporting workflows allows the assessment of an increasing number of scenarios, at an ever-faster rate, and enables skilled resources to focus on results rather than process. In-memory and distributed processing


provide the computing power necessary to run increasingly complex scenario-based risk models. All of this creates a less burdensome process and allows for a richer and more impactful analysis.

Leading institutions recognize that a modern scenario-based modeling platform prepares them for the future. As machine learning and artificial intelligence (AI) become more sophisticated, the evolution of models and processes will accelerate. Both efficiencies and strategic value will continue to improve.

For example, future modeling tools will leverage AI to improve data quality by computing missing values in the data and identifying anomalies. Stevenson notes: “Scenario-based analysis is highly dependent on data quality, and the higher the data quality the more relevant scenario-based risk assessment becomes to all aspects of the organization.”

Emerging technologies will also be able to identify where models no longer perform as needed and where they need to be rebuilt or recalibrated. Machine learning will uncover new ways to examine potential losses as well as uncover untapped potential that was not possible with limited data and modeling techniques.

THESE ARE COMPUTATIONALLY INTENSIVE EXERCISES THAT HAVE GROWN OVER TIME, REQUIRING INPUT FROM DISPARATE SYSTEMS AND MULTIPLE DATA HIERARCHIES.”— Paul Fagone, Partner, KPMG


CONCLUSION: SCENARIO-BASED MODELS BALANCE INNOVATION AND RISKWhile some banks may breathe a sigh of relief over the relaxing of supervisory stress test requirements, leading institutions are recognizing the business value created by a program of scenario-based risk management.


Scenario-based analysis enables organizations to evaluate their current exposure concentrations and identify and mitigate risks. It is also a valuable tool for strategic decision making. “When evaluating whether to introduce a new product, for example, organizations will be able to gauge how the new product will perform under a wide range of potential scenarios, and how various pricing models will impact the bottom line,” Kimner said.

Ongoing advances in technology allow scenario-based analyses to be performed faster and more efficiently than ever before. As a result, scenario-based risk management is moving from a compliance exercise to a tool for competitive advantage.

Key takeaways from this eBook:

� The use of stress testing in risk management, while largely driven by post-crisis regulatory pressures, has led to a better understanding of potential risks as well as improvements in the data, modeling and risk processes at financial institutions.

� As regulatory pressures ease, leading institutions are seeking to leverage these capabilities for business value and strategic advantage.

� To achieve this, banks must continue to address the challenges encountered in the supervisory stress test process, including: data integrity, operational inefficiencies, resource limitations and processing infrastructure.

� Technology, such as AI and machine learning, will improve scenario-based modeling capabilities, enabling organizations to assess risk using alternative models and benchmarks.

� Those institutions who have established efficient processes to quickly assess potential actions in a crisis will maintain a market advantage.


ABOUT THE SPONSORAs the leader in analytics, SAS has more than 42 years of experience helping organizations solve their toughest problems. Our unrelenting commitment to innovation enables banks to modernize and sustain a competitive edge.

SAS embeds AI capabilities in our software to deliver more intelligent, automated solutions that help companies boost productivity and unlock new possibilities. Through machine learning, natural language processing (NLP), forecasting and optimization, SAS supports diverse environments and scales to meet your changing business needs. With SAS® you can:

� Predict next-best communications, deliver real-time credit scoring and decisioning, and power chatbots to ensure a better customer experience.

� Stop payment fraud in real time, while reducing false positives and boosting investigator efficiency.

� Optimize both economic and regulatory capital using scenario-based stress testing.

SAS provides an integrated, enterprisewide risk management platform to help establish a risk-aware culture. For every way you manage risk in your organization – from strategic to reputational, operational, financial or compliance-related – SAS has you covered.

Our proven methodologies and best practices help you strike the right balance between short- and long-term strategies. They also empower decision makers and users to improve efficiency, transparency and profitability, making it possible to:

� Meet the challenges of expected credit loss modeling for IFRS 9 and CECL with an efficient, controlled, sustainable execution.

� Manage regulatory risk with award-winning data management and nearly real-time risk aggregation.

� Make enterprise stress testing less stressful with high-performance risk analytics. � Strengthen your risk strategy and oversight.

We’re proud that our customers include 96 of the top 100 companies on the Fortune Global 500®. We’ve also held a top-three spot in Chartis Research’s prestigious RiskTech100 report nine times in the past 11 years.

Learn more about how SAS is driving innovation and business value for risk professionals at sas.com/risk.

SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. 109961_G83534.0918

http://www.sas.com/risk

STRESS AND STRATEGY: A C-SUITE GUIDE TO SCENARIO- BASED ...

Documents