What is Risk Modeling?

Investments in effective risk management, and especially in IT systems to manage risk, have historically paid huge dividends. In a 2023 PwC US Risk Perspectives Survey, 57 percent of C-suite respondents reported seeing better decision-making capabilities thanks to investments in such applications. But there is still significant room for improvement in enterprise risk management, starting with better risk modeling and forecasting.

This article describes risk modeling and how it can help you make better business decisions across the whole enterprise.

What Is a Risk Model?

In simple terms, a risk model uses your business objectives and historical data to estimate the risk exposure your business might have in the present or future. Depending on your business’s nature, scale, and growth trajectory, your model might need various inputs — pricing risk, cyber risk, operational risk, reputational risk, and so forth — to create a mathematical projection.

Typical inputs to a risk modeling system

• ​Business objectives
• ​Risk appetite scoring
• ​Historical business data, including financial data
• ​Market trends
• ​Sets of scenarios for simulating risk exposure (credit risk, cyber risk, liquidity)

Based on these inputs, a risk model lists probable scenarios your business might encounter, including effects on your supply chain, business operations, or brand reputation.

The risk model will typically give two types of outcomes:

• ​Probability scenarios based on simulated risk exposure to internal or external events
• ​An expanded list of scenarios over a given period based on those first scenarios

More Businesses Are Using Risk Modeling: Here’s Why

Risk modeling helps a business to stay competitive. When a business uses risk models while setting strategy, they can anticipate how their plans might unfold in the real world; that leads to better execution and fewer mistakes.

Modern enterprises are complicated, with long supply chains and operations that process large data sets. These businesses also face significant data security challenges and regulatory compliance obligations due to their global footprint.

As a result, businesses must predict the risks they might encounter and which countermeasures would work best to keep those risks low. Companies plan to manage their risk exposure effectively through risk modeling.

For example, an online retailer with a global supply chain would want to predict how a global pandemic might disrupt its supply chain. The retailer might also want to ensure its online operations are protected against a Distributed Denial of Service (DDOS) attack, which means understanding how much stress-testing and preparedness the company has against unplanned downtime for a sustained period.

How to Integrate Risk Modeling Into Your Enterprise Risk Management (ERM) Strategy

If you want to be several steps ahead in planning and mitigating risk for your enterprise, then risk modeling and risk analytics will be crucial. To get started devising and implementing a risk model, try following these steps.

Make risk modeling an organization-wide priority

The strength and success of your efforts to deploy a risk model rely on your organization’s ability to generate the correct data and resources to make the model a reality. The people need to support the idea.

Ensure you have the proper leadership support and funding to get visibility from all the required decision-makers across your organization. You might also need each function to nominate risk owners who will be responsible for mitigating the risk exposure that your risk model computes at the end of the simulation.

Gather the correct data for the risk model

The risk model depends on the amount and quality of the data you feed into it. Break down organizational barriers to freely sharing qualitative data. Get the right external data as well to help with benchmarking your organization’s risks against larger peer groups. Accessing the best data should always be a priority from the very start.

Use technology to realize your risk model

Risk models can be computationally intensive depending on the scale of the business. You’ll need to deploy the right platform — one with automation and machine learning insights, to gather large data sets and compute deep insights.

Common risk modeling pitfalls to avoid

When implementing a risk model, know that the model’s ability to simulate risk relies not only on the data being fed into the model, but also the inherent risk mindset of your organization. For example, if your organization undergoes a turbulent period where sales are not picking up, the risk model might highlight the areas likely to be further disrupted.

Depending on your risk mindset, your leadership might take more conservative steps to protect your business – or it could take bolder, riskier steps with the potential to turn affairs around quickly. Same data, same risk model; but different courses of action depending on the company’s risk tolerance and appetite.

Being unaware of regulatory requirements

Depending on your company’s sector and scope of operations, you might have a wide range of regulatory obligations. Not including them in your risk model can result in misleading or inaccurate risk exposure, which might lead to regulatory issues and other challenges later on.

Deploying a risk model with limited inputs

A risk model works best when it pulls in data from across the entire organization, not just several specific functions. It’s vital that all parts of your enterprise participate.

If you only receive data from specific functions but not others, at best the model might illuminate risk exposures for those specific functions — but not for the organization as a whole, and the conclusions wouldn’t be useful for top leadership. Access to organization-wide data is critical.

Relying entirely on the risk model

The risk model will only be as effective as the inputs from your team. Some teams might read the risk model visualizations and immediately start preparing for high-probability scenarios, without additional business or domain knowledge considered in the model outcomes.

Don’t rely entirely on the model simulations. Share those conclusions with a group of business and domain experts before your team decides on remedial action.

Mitigate Risks for Your Business with ZenGRC

If your organization has decided to implement a risk model, you should align your risk strategy with a platform that supports your goals. RiskOptics makes enterprise risk management and risk modeling easier.

The ZenGRC platform factors in all the necessary regulatory and statutory compliance reporting requirements, so you need to feed in the proper historical data from your business to develop a comprehensive risk model.

To learn more about risk modeling and effective risk management with ZenGRC, schedule a demo today.