Enterprise Risk Management for Financial Institutions

In an ever-changing world, even minuscule developments in the social, political, or digital atmosphere can have big impacts on all enterprises. The financial systems of today are no different, as they are enterprises that thrive on building globalized linkages with people and nations. Consequently, a threat faced by one financial institution can become a cause for serious uncertainty and risks to the worldwide economy.

For instance, take the global financial crisis of 2008, where threats to a financial system were multi-layered and invasive enough to cause unprecedented damage for all players. It proved hazardous for the US banking system to begin with, but also dashed the dreams of millions of common people, investors, and developing institutions. In hindsight, it has provided us with important insights into how to assess, mitigate, and plan for the containment of risks inherent in international banking and valuation systems.

Today’s traditional risk management practices are insufficient for preventing the impairment of economic mechanisms across the globe. We need a system that uses past knowledge and ongoing developments to create more comprehensive risk management models. This is where contemporary models of Enterprise Risk Management (ERM) come in. This blog post explores the most relevant models and digital tools needed to mitigate risks for the financial institutions of today, while also preparing them for the future!

Enterprise Risk Management (ERM) is an advanced risk management strategy that takes a firm-level perspective to evaluate and reduce risks for an organization. It takes a top-down approach towards assessing the health of different branches or divisions of a firm, with the aim of identifying potential dangers to its operational goals. This information is then used to make management decisions that maximize a firm’s success by eliminating threats or possible roadblocks. 

ERM for financial institutions deals with a complex range of operational, credit and market risks. As each of these risk categories is unique in itself, special methods are constructed to oversee and manage their incidence within a company’s functioning. In order to monitor these risks in an integrated way, companies employ specialized models. This process of using sophisticated models to control a wide combination of risks in a business is called risk modeling. Model risk management offers a framework for governing risk mitigation for all threats associated with an institution’s activities. 

Just as in any other business, a financial enterprise employs specific methods to grow and expand itself. This endeavor comes with some risks that call for management and mitigation. Some common risks associated with the operations of financial institutions are: 

• Credit Risk – This is a classic case of borrowers failing to meet their contractual obligations on the loans they take from banks. Other cases include defaults on mortgages, credit card payments, and investment securities. 
• Market Risk – Financial institutions expose themselves to the unpredictability of market fluctuations by participating in the capital market. Investing in capital markets for equities, commodities or credit spreads exposes a bank’s asset base to changes in valuation due to demand and supply shifts. 
• Operational Risk – These are risks that occur due to human errors or systemic interruptions such as internal fraud, data breaches, or flawed management practices. Cybersecurity breaches resulting in hacking, customer phishing, or theft also belong to this risk category.

The above list is only a snapshot of the most common risks that financial enterprises face. In reality, there are infinite combinations of risks and complexities that accompany the operationalization of a financial infrastructure.
Let us now look at the 3 key principles crucial for any ERM framework used to mitigate risks like the ones listed above:

1. Identification

This refers to the process of assessing the internal and external atmosphere of an institution to spot potential risks. A catalog of activities the financial institution engages in is examined to quantify and categorize all possible threats to its operations. The data collected is analyzed to build risk models that functionalize a firm’s risk management strategy. 

2. Assessment

Once we are aware of the potential risks, it is imperative to assess their likelihood and the financial impact they could have on an institution. Both, direct risks such as natural disasters, and indirect or residual risks of a firm’s own activities are mapped. These are then fit into a categorical ERM framework, which predicts their occurrence rates and potential damage. Next, a strategy is formulated to provide a suitable response in the event such a risk does arise.

3. Management

This includes action plans focused on controlling, preventing, and mitigating risks in an institution. A detailed list of actions is defined to communicate priorities, assign responsibilities, and offer defensive measures against a probable threat. Continuous data gathering, monitoring, and predictive analysis of control responses is also a part of the risk management framework.

ERM frameworks often rely on risk models that utilize both quantitative techniques and machine learning algorithms. Yet any failures or miscalculations of a risk model arising from faulty modeling can cause acute damage to the operational processes of a company. This is where Model Risk Management becomes a crucial strategy that needs to be incorporated into all ERM plans. Let’s look into it in more detail.

A model is a system that relies on quantitative measures, economic hypotheses, and financial theories to aid institutions in making decisions and planning management approaches. Model risk arises when the model used for directing a firm’s operations fails to achieve its desired objectives. Instead, it might perform inadequately, mislead policy-makers, and cause adverse outcomes for a company. Model risk is an overarching risk across credit, market and operational risk. This implies that risks in any of these divisions can be managed through an overarching risk management model. This makes proper MRM strategies all the more crucial, since erroneous models can affect the internal decision-making process throughout the organization.

Model risk management refers to a group of processes and systems used to identify, measure, and reduce model risks within a firm’s operational framework. This involves oversight of risks to prevent adverse consequences of policy decisions based on incorrect models. Model risk management is of utmost importance for maintaining the long-term health and security of a financial institution. Some of the key benefits associated with Model Risk Management are:

MRM improves process efficiency in model development and validation while taking inventory of risky operative practices that can be eliminated in time.  As we know, an ounce of prevention is worth a pound of cure, and MRM proves to be useful in nipping defective models in the bud. This improves the chances of operational success at all levels, and minimizes the possibility of financial losses for a firm. This creates significant cost savings while meeting institutional objectives safely and quickly.

Model risk management helps in anticipating and mitigating potential model failures before they happen. This results in better consistency in operations and smoother governance of the firm. A well-managed financial institution stays in a position where it can address problems and setbacks at the earliest, leading to the harmonious functioning of its activities. Such an enterprise can thus stay compliant with regulatory standards like the SR 11-7 and SS1/23.

Model risk management allows organizations to potentially employ the best-ranking and thoroughly researched models for their enterprises. Proper validation allows relevant stakeholders to use systematically tested models for governance and policy execution, resulting in greater transparency and trust. This promotes better judgment and decision-making throughout the firm.

The field of finance is changing at an unprecedented pace by virtue of a fast-evolving technological landscape. In this day and age, every company, person, and nation is embracing digital solutions to solve their most relevant problems. The financial markets are no different, and even more so as machine learning and AI-based models have come to be increasingly used in their operations in recent years.

Once an institution starts using sophisticated models, it needs to embrace model risk management that is compatible with its level of complexity and impact. A dedicated model risk management technology provider like Yields can be instrumental in assisting an organization with robust model lifecycle management. The solution incorporates a dynamic model catalog to stay in control of your models and a configurable workflow engine to enforce compliance with your model risk management standards. Furthermore, once standardization is in place, clients can benefit from advanced automation capabilities to improve the efficiency and effectiveness of their model testing, validation and documentation processes. The award-winning solution enables model developers to focus on what they do best – developing models – while allowing model validation teams to efficiently handle repetitive tasks at scale. By choosing Yields, financial institutions can stay on top of regulatory standards and mitigate the risk of model failures in the most efficient and cost-effective way possible.

To find out if the Yields MRM solution suits your risk management needs, book a demo today!