By Jean Sullivan, vice president – insurance at Precisely
Insurance is an industry that is powered by data. Insurers have heavily depended upon statistical analysis to assess risk and price policies long before the rise of personal computers, the internet, and the wealth of analytical tools available today.
With the industry expecting more growth and profitability this year, insurers are increasingly dependent on emerging technologies and data sources to drive efficiency. This expected growth creates reliance on robust data management strategies to make optimal use of the information.
Difficulties with data management
The insurance industry’s dependence on data is complicated due to the challenges associated with the data-intensive process. Executives complain about having data that they cannot trust and access quickly. The lack of governance, structure, poor data quality, data silos, and lack of context prohibits decisive decision-making.
Data integrity addresses these problems holistically by integrating disparate data sources and proactively managing data quality to ensure accuracy, consistency, and completeness, adding context through data enrichment and location intelligence as well as providing an overarching governance framework. The data can become useful across the many business processes to improve profitability and reduce operation costs.
Investment in AI is accelerating
Like many other industries, insurers are increasingly investing in artificial intelligence (AI) and machine learning. These technologies are being utilised across many different applications, from fraud detection, improved pricing, and optimised claims management processes.
AI initiatives require trusted data to create the machine learning models and it’s important to be able to access information from a variety of sources across the enterprise in addition to third-party data.
AI systems must also be designed to accommodate change. For example, lockdown restrictions during the COVID-19 pandemic led to less traffic on the roads and fewer automobile accidents. Timely access to updated information ensures that AI investments are positioned to deliver the best results.
Employing IoT and mobile devices
Some insurers are exploring the use of IoT sensors as a tool to better understand and price risk. Carriers can use telematic devices and mobile phones to gather data about driving patterns, mileage, location, while other carriers accumulate data on actual usage time for insured equipment. Machines that sit idle much of the day presumably present lower risk than those that are used frequently throughout the day. These new tools can increase efficiency and improve pricing for carriers based on actual usage.
As the use of IoT devices increases, and as new applications for mobile technology are developed, we expect to see significant expansion of machine-generated data used in the insurance industry. With these larger volumes of data being collected, the need to manage data effectively will grow.
Location, location, location
The insurance industry is leveraging the use of location intelligence, and this begins with, “where is this building or structure located?”. However, such a simple question can be difficult to answer accurately and consistently across the enterprise. The need for hyper-accurate geocoding and a persistent and unique identifier serves as an important first step in the process of identifying the exact location. Then it is possible to layer enriched context to the location such as property attributes, points of interest, parcel boundaries, building information, dynamic weather, flood, and wildfire risk.
For insurers, an accurate and consistent geocode across the enterprise, with a persistent, unique identifier and enriched data allows carriers to understand the following: the risk within the four walls of a building, adjacent risk, co-tenant risk, distance to coast, elevation, whether the property is in a flood zone, whether the property is in the path of a storm with the largest expected hail precipitation, and what the potential total risk exposure is.
Similarly, wildfire risk is dependent on a property’s surroundings, taking in factors such as prevailing wind speed and direction, elevation, and proximity to combustible vegetation or other flammable material. Location intelligence provides this kind of rich tapestry of information that can shed light on a property’s risk profile and allows carriers to price accordingly.
Regulatory compliance from good data governance
Finally, the insurance industry is impacted by regulatory pressure with respect to data privacy, data sovereignty, and governance. The EU’s GDPR continues to evolve as cases make their way through the courts. Jurisdictions around the globe are considering similar legislation. Insurance regulators are eager to understand the risk models being applied by the companies they oversee. As the volume of data being used by insurers increases, regulatory scrutiny will continue to increase hence the need for a data integrity strategy.
As outlined, insurers have access to large volumes of data but are struggling with quick access to trusted data for actionable insights that improve their decision-making processes. Digital transformation, big data initiatives, and cloud-native analytics will complicate their usage hence the need for a data management strategy.
By focusing on the four pillars of data integrity – data integration, data quality and governance, location intelligence, and data enrichment, insurance companies can optimise the capabilities of data in their risk assessments.
