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The Data Infrastructure Behind Predictive Risk Analytics

Predictive risk analysis is a new set of sophisticated tools risk managers can use to optimize outcomes in risk and claims management.

These tools are incredibly valuable. They can help identify costly claims or those that are likely to be formally litigated. They can also help you understand how much money to set aside in reserves and how claimants are feeling about their process. Predictive analytics ultimately provide you with insights that you can use to increase revenue and cut costs.

Here’s the bad news: most risk managers aren’t making use of these powerful tools.

Why? Because setting up the data infrastructure can be complicated and expensive. To implement predictive risk analytics and capitalize on the value it can provide your company, you need several key components in place, including databases, a robust data pipeline, and accurate prediction models. 

In this article, you’ll learn what these components are, how to go about building the data infrastructure necessary to conduct powerful predictive risk analyses, and why purpose-built risk management software can cut down these costs. 

What Is Predictive Risk Analytics?

Predictive risk analytics refers to a set of analyses that produce predictions about what is likely to happen. It can give you advanced knowledge of emerging risks, potential losses, and looming threats to your business. These analyses use past and present claims data as the inputs and produce a variety of indicators that can be useful in understanding and managing risk.

Examples of predictive risk analytics that you can accomplish with Ventiv’s predictive analytics features include the following:

  • Case reserve predictions. You can predict the likely cost of a claim so that you can set aside sufficient reserves.
  • Claim severity predictions. You can evaluate claims to predict which are most likely to exceed cost thresholds, and then take steps to mitigate and lower their cost. 
  • Litigation potential. Formal litigation processes are expensive. You can use predictive analytics to forecast the likelihood that a claim will be litigated so you can take preventative measures.
  • Sentiment analysis. You can use natural language processing on texts to understand how a claimant is feeling, helping you better manage and prioritize claims.

Accurate forecasts are useful because they can help you make more effective strategic decisions. They can help you better prioritize your time, develop your budgets, and capitalize on new opportunities. 

The Components of Predictive Analytics and Risk Data Infrastructure

Predictive analytics can be extremely useful to risk managers. To effectively conduct these analytics, you need to have robust data infrastructure in place. Here are some of the most common components for such infrastructure. 

Risk Data

Predictive risk analysis models require a significant amount of data from both internal and external sources. Sources of relevant data can include:

  • Sales data like revenue, price points, and online store analytics
  • Finance data like cash flows, expense reports, and production reports
  • Customer data like demographics, surveys, and geographic locations 


You store your risk data in databases, which can be held in on-premise servers or the cloud. 

Typically, you’ll want to ensure that your IT team has a set of dedicated database administrators to properly set up and administer your databases. These professionals can ensure that your databases won’t get damaged and that your data is secure from cyber-attacks. 

Data Pipelines

A data pipeline is a series of data processing elements that consolidate, clean, and wrangle your data so that it can be easily analyzed. It typically connects data sources with the target repository—typically your risk data sources and your database. 

Your data pipeline should be designed to link to your risk management software tool so that it’s ready to apply data models and perform analytics. 

Data Models 

Your data gets turned into insights with data models. These models use statistical techniques, so when you build these models in-house, you typically need statisticians, data scientists, or other data modeling experts. 

Modern risk analytics software tools come with built-in data models, which helps reduce your reliance on having these experts on your team.

Risk Managers

Risk managers are the individuals who will review the results of predictive analytics and draw insights that can be used to make business decisions. They turn the model outputs into strategy, so they are ultimately who generate value from your risk data. 

Digital Transformation Consultants

Developing a full set of data infrastructure to enable predictive analytics can be overwhelming, especially if you don’t have the experience in-house. Digital transformation consultants can fill the gap, albeit usually for a hefty fee. They can provide crucial advice and strategy to ensure your data infrastructure project is successful. 

The Right Software Can Simplify Predictive Analytics

Predictive risk analytics generate significant value, but building the data infrastructure capacity to conduct such analyses can demand a large investment—especially if you’re building it all in-house.

Ventiv’s risk management analytics software provides you with predictive analytics capacity without requiring all of that infrastructure. 

Once you connect our platform to your data, you can use our built-in predictive risk analytics models to quickly and easily generate useful insights. These models apply AI and machine learning to help you achieve the best results for your claims and underwriting—no expensive consultants or data scientists are required.

Here’s another value-add: Our team provides you with guidance on how to make the most of your data and navigate easily through the different segments of a claim. 

Book a demo to see how our predictive risk analytics can help you no matter where you are in your data journey. 

Jun 8, 2023

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