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Construction Quality

Predictive-Based Safety—Saving Lives, Shaping Construction Insurance

Rose Hall | December 6, 2019

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As more and more industry firms adopt construction technology, business leaders are exploring creative ways to harness artificial intelligence (AI) and leverage big data for more than just "interesting" insights. In particular, we are looking for ways to have a greater influence on the entire construction project life cycle, including insurance.

AXA XL and are teaming up to explore what's possible for the future of construction insurance when we wield the immense power of predictive analytics in the field.

What Is Predictive-Based Safety?

At its core, Predictive-Based Safety (PBS) is about using data to change behaviors. While the behavioral-based safety systems currently in widespread use on construction projects today have brought recordable incident rates down significantly, it is by applying technology and data together to drive behaviors that will take contractors to the next level of safety success.

There are three elements of a PBS program: data collection, data transparency, and prediction deployment. Each of these elements requires training related to the application of appropriate behaviors by those involved.

Data Collection

The predictive models that enable PBS require data from several sources, including safety observations, safety incidents, and other project data. Collecting data on interactions between observers and employees in the work environment by observing safety behavior is essential if PBS is to be implemented correctly. These observation interactions must be purposeful and have safety at the center. The observers should be trained in guiding the interaction and hazard recognition and collecting meaningful observations.

There are many tools available to collect observation data. However, those purpose-built for applying AI and predictive analytics collect the data differently. They collect both direct user input and background data that predictive models use to rate and rank each observation. The data are collected and stored in a way that allows them to be understood and integrated by predictive models and business intelligence software.

The second source of data is from incidents that take place on each project. Incident data collection should include every single incident that takes place on all projects, including near misses/hits, property damage, equipment damage, third-party incidents, and, of course, injuries of all types, regardless of how minor. The data from these incidents serve as really powerful indicators of safety risk. For many contractors, reporting at this level of detail requires a change in both the process and procedures. They may even need to use new tools to collect the data digitally. Contractors who build a culture of reporting all incidents are often surprised at how many incidents went unreported before having the culture, tools, and processes in place.

Additional enterprise data are necessary to round out predictive models. To achieve this, it is important that a contractor's IT department works to integrate data across their organizations. The insights that can be gained from combining and overlaying data from different sources can be staggering and redefine the rules of thumb the organization has employed for years or decades.

Data Transparency

Providing full data transparency is key to implementing a successful PBS program—but it comes with a catch. Once a meaningful data set is assembled, the next challenge becomes managing reactions to the data. For many organizations, having full transparency in a near-real-time data set is something they have never experienced. PBS includes a set of expectations with regard to viewing and interpreting the data. Reacting poorly to the data results in a hesitancy on the part of observers to report what they are actually observing, skewing the data driving predictive models or worse—it could lead to the refusal by many observers to use the data collection tools rendering PBS ineffective.

Prediction Deployment

The outputs that come from a predictive model take two forms: predictions and prescriptions. Predictions answer the question, "Which job is most at risk of an incident?" Prescriptions are actions that project teams can take to intervene and reduce the risk of the prediction actually occurring. Prescriptions answer the question, "What should I do?" Teams need to be trained on the use of the insights provided by the model and on how to interpret them.

With predictions and prescriptions in hand, project teams are able to focus in on the details of their safety program that require attention rather than use the shotgun approach that is so common today. Their actions are purposeful and deliberate with the goal of "proving the model wrong." At the executive level, predictions help narrow the list of projects to help operations and safety executives focus support resources where they are most needed. Effective deployments of PBS have seen reductions in contractor-recordable incident rates as high as 60 percent in the first year.

Potential Insurance Implications

PBS has the capability to not only change the way we build but also the way we insure the way we build. Traditional workers compensation premiums are developed starting with National Council on Compensation Insurance rates, then factoring in credits or debits based on losses, Experience Modifier Rate, and the contractor's safety practices. These elements are standard underwriting data points for determining reliable indicators of estimated future results. While the aforementioned methodologies have served well thus far, what if there was a tool available that goes beyond "standard"? A tool that could add additional credible value to reduce losses and better predict future premium costs.'s AI engine touts an impressive prediction accuracy rate as high as 86 percent and commensurate reductions in the occurrence of recordable incidents as high as 60 percent. With this level of confidence, data and AI may soon be able to influence workers compensation premiums, much like your personal auto insurance telematic plug-in could score you a "safe-driver discount" after observing your actual driving habits for a period of time, rather than rating you based on your past history and that of other drivers "like you."

Both for safety and other construction risk metrics, using real-time data to assess actual risk versus past risk may allow insurers to better predict future results in their underwriting and provide more personalized customer service. It's conceivable that with enough data, the commercial world could mirror the emerging innovations that we are seeing in the consumer market, including customizable policies, supported by personalized risk engineering services, and more streamlined claims processes, all courtesy of big data and AI. What if you could save lives and reduce your premium in real time? With predictive-based safety, this may soon be a reality.

Many thanks to Tim Gattie for assistance with this article. Gattie is vice president of Industry Strategy at He has 20 years of construction industry experience working for regional, national, and international general contractors in roles ranging from field engineer to project director. Mr. Gattie is passionate about using technology as a tool to improve the way construction projects are delivered and is eager to share his story with others in the industry. He graduated from the University of Pittsburgh with a bachelor's of science in civil and environmental engineering and is a Registered Professional Engineer in Arizona, California, and Utah. enables the architecture, engineering, and construction industry to significantly reduce jobsite risk by combining the best of human intelligence and AI.'s Safety Suite includes Safety Observations, Safety Monitoring, and Predictive Analytics products that combine to give project teams and management the ability to predict and prevent incidents—saving lives and preserving timely project delivery.

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