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Understanding the Risk Management Analytics Landscape

Proper risk management is, without question, a critical component of enterprise resilience in today’s business environments. Unfortunately, increasing reliance on complex value chain relationships and expanding regulatory constraints make it challenging for organizations to both manage and understand risk; when it comes to the latter, acquiring and integrating concrete data across disparate datasets is crucial for developing the necessary awareness to design effective risk management strategies. Subsequently, forward-thinking organizations are incorporating advanced analytics into their risk management practices to glean key insights regarding their enterprise risk posture.

In this article, we’ll explore the emerging risk management analytics landscape and related advances across 4 categories: performance, analytics, risk prevention, and decision sciences.

Defining Risk Management Analytics

Risk management analytics can be defined as the systematic computational analysis of enterprise risk data—discovering and making sense of significant patterns in data  to identify weaknesses, threats, and barriers to performance/growth in the organization. For example, a firm may wish to identify and address the areas or departments with the highest claim occurrences or forecast future trends in anticipation of potential risk drivers. A risk management analytics platform makes these crucial discoveries easily accessible, allowing risk managers to automatically measure, quantify, and predict risk with a high degree of certainty. With this new risk awareness and visibility, organizations are no longer relegated to traditional, subjective assessments and manual, error-prone data handling measures.

As of late, notable advances in risk management analytics have taken place in the following areas:

1. Analytics

Advanced analytics go beyond the data residing in the rows and columns of databases/spreadsheets to provide advanced capabilities such as benchmarking against internal goals and industry peers. Additionally, advanced analytics help to unify and elevate the risk conversation across the organization by expanding upon existing data-driven insights with advanced charting and dashboarding. The resulting holistic view of enterprise risk enables risk managers to comprehensively address underperforming areas and set measures for course correction.

2. Performance

As a function of business intelligence, performance risk analytics give organizations the appropriate models, tools, and processes for converting raw risk data into meaningful, actionable day-to-day risk management information. Organizations may rely on performance risk functionality for ongoing reporting on how the business is performing, as well as ad-hoc services that uncover hidden trends and key risk drivers using technologies such as natural language processing (NLP). With this data and related insights, firms can anticipate risk trends, uncover problems and bottlenecks in daily operations, and re-route/reprioritize resources as needed.

3. Risk Prevention

Advanced risk management analytics enables enterprises to expand their reach beyond increasing volumes of structured data (e.g., databases, spreadsheets) within the organization; risk analytics may also integrate both internal and external, unstructured data (e.g., websites, social media), as well as data resident in maps and images for a streamlined, unified view of the organization’s risk exposures. 

The resulting comprehensive view of the organization’s risk posture plays an instrumental part in enterprise risk prevention, as it allows for an “around-the-corner” view for better anticipating where risks may emerge across the organization, as well as which risk factors may be related or codependent. Additionally, advanced risk management analytics provide the capability to address unforeseen risks such as weather and natural disaster events—for example, Ventiv's predictive analytics risk management solution helps organizations anticipate natural disasters and troubleshoot geographically-specific risk areas within their buildings and properties.

4. Decision Sciences

Human efforts fall woefully short when it comes to gleaning timely, actionable insights from the massive volume of data continuously created by the organization. For this reason, AI/ML-driven analytics and their underlying algorithms are fast becoming a hard enterprise data collection/processing requirement. Risk management analytics draws from the decision sciences domain to empower organizations with better predictive capabilities—specifically, by including AI/ML features that augment risk-based decision-making. 

Advanced risk analytics platforms that incorporate AI/ML also enable data scientists to quickly build and deploy custom models, tailor-built for their unique requirements. With AI/ML, risk managers can monitor and evaluate risk in real-time for faster, more effective decision making.

Armed with more contextual, deeper data insights and analytics, risk managers are better positioned to develop more actionable risk programs/initiatives, measure their respective levels of efficacy, and identify areas of improvement. And because advanced analytics are automatically generated and consistently delivered, organizations can use them in developing continuous risk posture improvement processes.

The risk analytics management landscape is fast evolving to address the widening array of enterprise risk-related decisions underpinning strategic and operational objectives. With today’s risk management analytics tools, technologies, and techniques, firms can better anticipate future risk scenarios, measure multiple outcomes, and acquire critical insights for bolstering enterprise resilience. To learn more about how to navigate today and tomorrow’s risk management analytics landscape, contact Ventiv today for a consultation.

 
 

Jun 5, 2023

 | Originally posted on 

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