There is no doubt that the insurance industry depends on reliable data. Before the advent of computers, the Internet, and widespread analytical tools, insurance companies relied heavily on technology-enabled mathematical calculations and statistical analysis to assess risk and calculate and set policy prices.
Today, insurance companies have access to more data than ever before. This is a double-edged sword: larger amounts of data provide significantly more input for informed case decisions regarding pricing, policy design and risk coverage, but many companies struggle with the volume and variety of data available and the speed with which they can be processed receive this information.
At the beginning of 2022, some technology trends for the rest of the year and the near future are already emerging. In many cases, this is the continuation and acceleration of technology trends in the insurance industry that have already begun. As big data and cloud-native analytics emerge as a driving force across all industries, the insurance industry is feeling the impact in particular.
Data management is more of a challenge than ever
It should come as no surprise that the first trend revolves around the continued growth of big data and cloud-native analytics as a key enabler in the insurance business. The data-intensive processes that are so important to the insurance industry come with numerous challenges.
Managers complain that they have too much information. They often face a lack of data governance and structure, and poor data quality is a common problem. In addition, data often resides in silos and there is a lack of effective mechanisms for reliable and consistent integration. The timely transmission of information often does not meet the standards required by insurance companies. In addition, information often lacks context, preventing users from realizing their full potential.
Is the data structured? Are they managed? Are the correct data being used? The answers to these questions determine the use of the data in the actuarial and underwriting process and thus also its value for the development and maintenance of a profitable insurance portfolio.
Data integrity addresses these issues holistically, enabling the integration of disparate data sources, delivering data where and when it is needed, and proactively managing data quality to ensure accuracy, consistency, and completeness. In addition, data enrichment and location intelligence continue to add context to data and provide an overarching governance framework.
Investments in AI are increasing
Unsurprisingly, investment in artificial intelligence (AI) is increasing among insurance companies. AI and machine learning are being used for an ever-widening range of applications, from fraud detection and technical pricing to optimizing claims management processes.
Yet AI initiatives often face headwinds in the form of poor data integrity. Machine learning models are only as good as the data used to train them. Data quality is paramount, and access to information from a variety of sources across the organization (as well as third-party data) is important.
Equally important, AI systems must be designed to adapt to change. Risk profiles are constantly changing. The COVID pandemic, for example, led to less traffic on the roads, which in turn led to fewer car accidents. Timely access to updated information ensures AI investments are positioned to deliver optimal results.
The proliferation of the “Internet of Things” and mobile devices
Some insurance companies have already started investigating the use of IoT sensors as a tool for better risk understanding, and some pioneers already have offerings in this area. In the auto insurance space, telematics devices and mobile phones provide detailed information about driving behavior and location, but there are other applications, such as using IoT devices to determine actual usage time of insured devices. Devices that are rarely used probably pose a lower risk than those that are used several times a day. Just as auto insurance companies use actual mileage to set drivers’ rates for the coming year, commercial property insurers can refine their risk models based on detailed information about actual usage.
With the proliferation of IoT devices and the development of new applications for mobile technology, we expect a significant expansion of machine-generated data used in the insurance industry.
Location data more important than ever
The use of mobile technology serves as a kind of bridge to the next topic: location. The insurance industry used to rely on crude information broken down by a five-digit zip code or number pad. That has changed because the detailed information available has grown exponentially.
Much begins with a relatively simple question: “Where is this building (or this person or this car)?” However, answering this question accurately and reliably can be difficult. Effective geocoding is a very important first step in determining the location of an entity. With this information, a whole world of data and attributes is available, adding rich context to the location in question.
This information can be very important for insurance companies. Take, for example, the case of an insured motorist whose home is on a corner lot. There is a busy street on one side and a quiet side street on the other. How high is the risk of accidents emanating from this location? This may depend on the location of the policyholder’s driveway. If the entrance is on the main road, the risk may be higher. If it is on a side street, the risk for the driver is probably much lower.
The risk of forest fires also depends on the area surrounding a property. These include factors such as prevailing wind speed and direction, elevation, and proximity to combustible vegetation or other flammable material. Location intelligence provides a wealth of information that can provide insight into a property’s risk profile.
Data governance ensures compliance with regulations
Finally, we expect ongoing regulatory pressures related to privacy, data sovereignty and data governance. The European General Data Protection Regulation (GDPR) is evolving with the corresponding requirements, as many cases are now being heard in court. Other countries around the world are considering similar legislation. Insurance regulators are also keen to understand the risk models used by the companies they regulate. Regulatory scrutiny will continue to increase as the amount of data used by insurance companies increases.
Looking ahead, the case for strong data integrity programs will only get stronger. Data integrity helps organizations build trust in their data so their users can easily derive important insights from their data.