What is risk analytics?

Risk analytics is a set of techniques that measures, quantifies, and predicts risk with a large degree of accuracy. Risk management is not new and has been the key responsibility of management for years. Recently, the awareness of risk has increased. Media outlets often report on breaches, lax processes, data hacks, or poor systems. Regulatory and economic environments are stricter, and risk management is more important than ever before.

With the rise of big data, enhanced computing capabilities, and advanced analytics, companies can leverage the power of data. Artificial intelligence, machine learning, the Internet of Things, and even the use of drones are some of the tools businesses employ for better decision-making. These tools are crucial for risk managers, who apply these techniques to risk management to identify, measure, and mitigate risk.

The need to better understand key risk factors and their impact on a company’s bottom line has never been more important. Risk factors help identify future risks before they become a larger problem. Risk management encompasses the management of legacy systems and historical protocols--where risk analytics can make a huge difference.

Why is there a need for risk analytics?

Risk management requires preparation for worst-case scenarios, especially since the world is more automated and digitized than ever. In this digital world, hackers are becoming increasingly intelligent, resourceful, and creative. As technology brings the global market together, risk management requires more tasks, resources, and deliverables--involving more stakeholders in every enterprise.

In many cases, risks are hard to identify, leading to instinct-based risk management. In such cases building risk-mitigating strategies can be quite difficult. Managers are asked to provide substantial evidence to back up their theories of risk to better communicate the various risk factors. Legacy systems can hinder this process, which is why risk analytics is needed. Risk analytics collects and presents the data to make the right decisions.

The techniques used in risk analytics lend credibility and data-based validation to manager’s opinions. With the expansion of businesses, the ability of individuals to predict risks from a macro or micro point of view is almost impossible. Analytics comes to the rescue.

With risk analytics, companies can create a baseline to measure risk against variables for the whole organization. Several risk possibilities can be brought onto a single platform, giving executives the clarity they need to identify, view, understand, and manage risks. Creating this unified approach to risk management is central to becoming a data-driven enterprise; one where management and executives integrate all possible risk considerations to arrive at strategic decision-making.

Which businesses use risk analytics?

For any organization, finding the right balance between risk and innovation is important. Every organization and vertical must employ techniques to remain competitive, compliant, and functional.

Banking, financial services, and insurance

Risk analytics are used in the financial sector, particularly during the forecast period. The banking sector is a vertical extensively dependent on system integration, modeling, quality of data, and its sourcing. Banking is highly regulated and monitored, and there are substantial penalties if standards are not met.


With each successive forecasting period, the telecommunications sector is expected to invest more in risk analytics. There is stiff competition in the sector, resulting in reduced revenues. At the same time, the sector is plagued with fraud and has lost billions. Several telecom operators are turning to risk analytics solutions to stem revenue leakage and increase revenue.

Government services

The government services sector uses risk analytics solutions intensely, not just for risk prediction and pre-empting, but also for diverse activities such as weather forecasting, border security management, policy control, and calculated decision-making. Successful attacks on governments over the past few years have highlighted the weakness of systems and the ramifications of these hacks.

Healthcare sector

Among healthcare organizations, risk analytics determines and ensures patient safety. It reduces the chances of drug contamination and also helps to control, store, and access user data.

There are other sectors that need to heavily invest in risk analytics:

  • Consumer goods
  • Retail
  • Manufacturing
  • Transportation and logistics
  • Information technology
  • Media
  • Energy and utilities
  • More sectors are investing in risk analytics for the coming financial years.

Benefits of risk analytics

Concrete information, no guesswork

Many organizations’ primary concern with risk management is its guesswork. Luckily, risk analytics removes the guesswork and provides organizations with concrete data, which can be used to create actionable protocols. Organizations can apply a wide range of techniques and technologies to the data to extract insights, examine a wide range of scenarios, and make predictions.

Integration of structured and unstructured data

Every organization brings in vast amounts of data from structured and unstructured sources. Structured sources include data stored in databases, while unstructured sources refer to information that is gathered from a company’s website, social media, video, photography, or non-database information. All of these sources (and the vast volumes of data) are available to the company, both internally and externally. With risk analytics, managers can bring all of this data onto a single platform to view, analyze, and create actionable insights.

Achieve company-wide impact

In the course of creating effective risk strategies, management teams can lose sight of the end goal, which is ensuring positive outcomes for the entire company. This is especially relevant if information is contained in siloes. Since risk analytics pulls up data onto a single platform, its impact is enterprise-wide.

Applying risk insights to decision-making

Risk spans an entire organization, often crossing and overlapping all administrative barriers. Even with risk-related insights, knowing what to do with them can be quite a task. With risk analytics, organizations can sort data to help management develop foresight into potential risks and establish problem patterns. Risk analytics lays the groundwork for data insights.

6 steps to implementing risk analytics

Considering that every department in an organization has a different viewpoint of risk, chances are, independent risk analyses will result in confusion or overlap. Each department may have its own parameters and reference factors. For successful risk analytics, organizations need a single source of data to manage risks company-wide.

The primary objective of any risk management program is to minimize risk. Companies have to evaluate their risk tolerance levels and ensure that programs reduce risk accordingly. There are six key steps to making risk analytics work in an organization:

1. Create a library of possible risks

Having a thorough library of risks is the perfect starting point to risk assessment. The risk factors section should be publicly accessible to all departments and have multiple entries on possibilities from all departments.

Organizations create a list of potential and known risks and all the factors that could lead to them. This list answers questions such as, what are the common problems that arise, what happens when these problems arise, and what triggers these issues?

Alternately some organizations offer curated libraries that are plug-and-play in nature, saving a good deal of time. With a risk factor list, organizations can plot scorecards and heat maps to raise red flags when risk is predicted to occur. This library can view risk impact and potential risks simultaneously.

2. Review and test data sources

An organization also needs to define its key risk indicators. Once done, the next step is to spot test data and validate its crucial risk indicators. Analytics will not be running at this point, which is why spot testing is necessary with individual systems. This helps confirm and validate the chosen risk indicators.

Managers need to analyze risks, quantify their impact, and clearly outline the possible consequences of each risk on the business. They also need to identify who controls impact and defines impact conditions. This is where managers identify the cost and chances of risk occurring at all.

At the enterprise level, the list of risks may be inter-linked across several business units. It investigates portfolio effects, where individual business unit risks may end up canceling each other out. This is the step at which risks present within a business or across an enterprise are aggregated.

There should be refinement of risks and their impact. Refinement is needed to assess risk at the unit level of a business and to estimate costs and probability of occurrence. Next, these risks are refined to relate to the priorities of the business unit based on management objectives. From here, it is further refined to the corporate level.

Companies must address the risks at hand and decide which are worth pursuing. Some organizations may choose to eliminate risks considering their steep costs and negative consequences. In other cases, reducing impact may be the goal. This can be done by reducing the probability of the risk-taking place at all or by having some solutions in place to soften the impact. Companies can consider insuring the risk completely or partially based on cost versus benefit calculation.

3. Consolidate data sources and automate testing

All data is placed on a single software platform. A monitoring system is set up and keeps continuous tabs on tracking risks, reporting them, sending out alerts where needed, and constantly assessing potential risk factors.

Additionally, organizations can apply scheduled analytics that will constantly test and validate controls. Since the tests are automated, any red flag raised sends an intimation to the management inbox, allowing employees to push the issue directly into the solution phase.

4. Harness the power of visualization

Data is filled with unprocessed insights. While the analytics may look through risk factors to help identify red flags in time, tracking data and its patterns outside of those factors can make a difference. Risk monitoring dashboards use visualization methods that make it simpler to spot newer insights and quantify risks, enabling organizations to look beyond the obvious.

5. Report insights

With visualizations and insights in place, the next step is to demonstrate that all the information is in place. It is important to have the right software working to communicate this. Visualizations ensure that people get the point and increase impact.

Organizations must review the insights regularly. This process is based on facts and dynamic risk assessment. Organizations can learn a lot from successes, failures, and approaches.

6. Expand, and do it again

Once the organization is used to the idea of risk analytics, how it works, and its benefits, the concept can be applied across departments and onto a single platform. Knowledge, processes, and methodologies can be shared and customized. The more data the system has access to, the more testing can be done.

The importance and future of risk analytics

Although risk analytics models may vary with organizations, the larger principles of risk assessment remain the same and are well rooted in risk management. By establishing a strong framework, organizations can leverage the benefits of risk analytics models. This not only keeps companies and employees safe, but also ensures they remain competitive in a data-driven world.

Risk analytics diagram

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